thesis data mining topics

82 Data Mining Essay Topic Ideas & Examples

🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.

Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
Data Mining Classifiers: The Advantages and Disadvantages One of the major disadvantages of this algorithm is the fact that it has to generate distance measures for all the recorded attributes. We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […]
Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
Data Mining and Its Major Advantages Thus, it is possible to conclude that data mining is a convenient and effective way of processing information, which has many advantages.
Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
Data Mining in Social Networks: Linkedin.com One of the ways to achieve the aim is to understand how users view data mining of their data on LinkedIn.
Ethnography and Data Mining in Anthropology The study of cultures is of great importance under normal circumstances to enhance the understanding of the same. Data mining is the success secret of ethnography.
Issues With Data Mining It is necessary to note that the usage of data mining helps FBI to have access to the necessary information for terrorism and crime tracking.
Large Volume Data Handling: An Efficient Data Mining Solution Data mining is the process of sorting huge amount of data and finding out the relevant data. Data mining is widely used for the maintenance of data which helps a lot to an organization in […]
Data Mining and Analytical Developments In this era where there is a lot of information to be handled at ago and actually with little available time, it is necessarily useful and wise to analyze data from different viewpoints and summarize […]
Levi’s Company’s Data Mining & Customer Analytics Levi, the renowned name in jeans is feeling the heat of competition from a number of other brands, which have come upon the scene well after Levi’s but today appear to be approaching Levi’s market […]
Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
Data Mining in Healthcare: Applications and Big Data Analyze Big data analysis is among the most influential modern trends in informatics and it has applications in virtually every sphere of human life.
“Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry First of all, the article generally elaborates on the notion of customer relationship management, which is defined as “the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company”.
Data Mining Techniques and Applications The use of data mining to detect disturbances in the ecosystem can help to avert problems that are destructive to the environment and to society.
Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
Data Mining Technologies According to Han & Kamber, data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases […]
Data Mining: A Critical Discussion In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical […]
Commercial Uses of Data Mining Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations.
A Discussion on the Acceptability of Data Mining Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much […]
Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
Applebee’s, Travelocity and Others: Data Mining for Business Decisions
Applying Data Mining Procedures to a Customer Relationship
Business Intelligence as Competitive Tool of Data Mining
Overview of Accounting Information System Data Mining
Applying Data Mining Technique to Disassembly Sequence Planning
Approach for Image Data Mining Cultural Studies
Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
Database Data Mining: The Silent Invasion of Privacy
Data Management: Data Warehousing and Data Mining
Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
Data Mining and Its Impact on Healthcare
Innovations and Perspectives in Data Mining and Knowledge Discovery
Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Linking Data Mining and Anomaly Detection Techniques
Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
Credit Card Fraud Detection Through Data Mining
Data Mining Approach for Direct Marketing of Banking Products
Constructive Data Mining: Modeling Argentine Broad Money Demand
Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
Commercially Available Data Mining Tools Used in the Economic Environment
Data Mining Climate Variability as an Indicator of U.S. Natural Gas
Analysis of Data Mining in the Pharmaceutical Industry
Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
Credit Evaluation Model for Banks Using Data Mining
Data Mining for Business Intelligence: Multiple Linear Regression
Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
Data Mining for Fraud Detection Using Invoicing Data
Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
Data Mining for Industrial Engineering and Management
Business Intelligence and Data Mining – Decision Trees
Data Mining for Traffic Prediction and Intelligent Traffic Management System
Building Data Mining Applications for CRM
Data Mining Optimization Algorithms Based on the Swarm Intelligence
Big Data Mining: Challenges, Technologies, Tools, and Applications
Data Mining Solutions for the Business Environment
Overview of Big Data Mining and Business Intelligence Trends
Data Mining Techniques for Customer Relationship Management
Classification-Based Data Mining Approach for Quality Control in Wine Production
Data Mining With Local Model Specification Uncertainty
Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
Enhancing Information Management Through Data Mining Analytics
Evaluating Feature Selection Methods for Learning in Data Mining Applications
Extracting Formations From Long Financial Time Series Using Data Mining
Financial and Banking Markets and Data Mining Techniques
Fraudulent Financial Statements and Detection Through Techniques of Data Mining
Harmful Impact Internet and Data Mining Have on Society
Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
Integrating Data Mining Techniques Into Telemedicine Systems
Investigating Tobacco Usage Habits Using Data Mining Approach
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105 Data Mining Essay Topic Ideas & Examples

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Data mining is a powerful tool that helps businesses and organizations uncover hidden patterns, trends, and insights from large datasets. It involves the process of extracting valuable information from raw data, which can then be used for various purposes such as improving decision-making, predicting future outcomes, and understanding customer behavior. If you are a student or a professional looking to write an essay on data mining, here are 105 topic ideas and examples to help you get started.

The importance of data mining in today's business world
Ethical considerations in data mining
The impact of data mining on privacy
How data mining is used in healthcare to improve patient outcomes
Predictive analytics: Using data mining to forecast future trends
Data mining techniques for fraud detection in financial institutions
The role of data mining in customer relationship management
The use of data mining in social media marketing
Data mining and its application in personalized advertising
The benefits of data mining in supply chain management
Text mining: Analyzing unstructured data to extract valuable insights
The challenges of big data mining
Data mining in e-commerce: Enhancing customer experience
The role of data mining in improving cybersecurity
Data mining and its impact on decision-making in organizations
The use of data mining in predicting stock market trends
Data mining and its role in recommendation systems
The benefits of data mining in the education sector
Data mining techniques for sentiment analysis
The ethical implications of data mining in government surveillance
Data mining in the gaming industry: Enhancing player experience
The role of data mining in personalized medicine
Data mining techniques for credit scoring and risk assessment
The use of data mining in sports analytics
Data mining and its impact on urban planning
Data mining and its role in weather forecasting
The challenges of data mining in social network analysis
Data mining techniques for detecting plagiarism in academic papers
Data mining and its application in predicting natural disasters
The role of data mining in improving transportation systems
Data mining and its impact on online dating platforms
Data mining for predicting customer churn in telecommunications industry
The use of data mining in optimizing energy consumption
Data mining techniques for detecting credit card fraud
Data mining and its role in personalized news recommendation
The benefits of data mining in human resources management
Data mining in healthcare for disease diagnosis and treatment
Data mining and its impact on online advertising
Data mining techniques for identifying patterns in gene expression data
The role of data mining in improving online learning platforms
Data mining and its application in criminal investigations
The use of data mining in optimizing manufacturing processes
Data mining techniques for predicting customer lifetime value
The benefits of data mining in predicting traffic congestion
Data mining and its role in predicting customer preferences
Data mining in environmental analysis and conservation efforts
Data mining and its impact on personalized financial planning
The challenges of data mining in healthcare data integration
Data mining techniques for analyzing social media sentiment
The role of data mining in improving public safety
Data mining and its application in fraud detection in insurance industry
The use of data mining in optimizing online search engines
Data mining techniques for predicting student performance in education
Data mining and its impact on improving online user experience
Data mining and its role in predicting customer satisfaction
The benefits of data mining in optimizing logistics and supply chain
Data mining in crime analysis and prevention
Data mining and its impact on personalization in online shopping
Data mining techniques for analyzing customer feedback and reviews
The role of data mining in improving healthcare resource allocation
Data mining and its application in predicting customer lifetime loyalty
The use of data mining in optimizing inventory management
Data mining techniques for detecting fraudulent insurance claims
Data mining and its role in predicting disease outbreaks
Data mining in sentiment analysis of political discourse
Data mining and its impact on improving online voting systems
The challenges of data mining in analyzing geospatial data
Data mining techniques for optimizing pricing strategies in retail
The benefits of data mining in predicting customer churn in telecom industry
Data mining and its role in improving road safety
Data mining and its application in predicting customer behavior
The use of data mining in optimizing energy distribution networks
Data mining techniques for detecting insider trading in financial markets
Data mining and its impact on personalized travel recommendations
Data mining and its role in predicting customer loyalty
The benefits of data mining in optimizing warehouse operations
Data mining in fraud detection and prevention in online transactions
Data mining and its impact on personalized healthcare recommendations
Data mining techniques for analyzing customer segmentation
The role of data mining in improving disaster response and recovery
Data mining and its application in predicting customer lifetime value
The use of data mining in optimizing fleet management
Data mining techniques for detecting money laundering activities
Data mining and its role in predicting customer preferences in online advertising
The benefits of data mining in optimizing service quality in hospitality industry
Data mining in predicting student dropout and improving retention
Data mining and its impact on personalized music recommendations
Data mining techniques for analyzing patterns in web usage data
The role of data mining in improving urban mobility and transportation systems
Data mining and its application in predicting customer satisfaction in retail
The use of data mining in optimizing healthcare resource allocation
Data mining techniques for detecting online identity theft
Data mining and its role in predicting customer lifetime loyalty in e-commerce
The benefits of data mining in optimizing delivery routes
Data mining in detecting patterns of online extremist behavior
Data mining and its impact on enhancing personalized learning experiences
Data mining techniques for analyzing customer churn in subscription-based services
The role of data mining in improving disaster risk reduction strategies
Data mining and its application in predicting customer behavior in online gaming
The use of data mining in optimizing maintenance schedules for industrial equipment
Data mining techniques for detecting healthcare fraud and abuse
Data mining and its role in predicting customer preferences in online travel booking
The benefits of data mining in optimizing waste management processes
Data mining in detecting patterns of cyberbullying behavior
Data mining and its impact on enhancing personalized financial advice

These topic ideas provide a wide range of options for your data mining essay. Whether you are interested in business applications, healthcare, social media, or any other field, there is a topic that suits your interests. Remember to choose a topic that you are passionate about and conduct thorough research to provide a well-informed and insightful essay on data mining.

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How does data mining work?

A standard data mining design begins with the appropriate business statement in the questionnaire, the appropriate data is collected to tackle it, and the data is prepared for the examination.
What happens in the earlier stages determines how successful the later versions are.
Data miners should assure the data quality they utilize as input for research because bad data quality results in poor outcomes.
Establishing a detailed understanding of the design factors, such as the present business scenario, the project’s main business goal, and the performance objectives.
Identifying the data required to address the problem as well as collecting this from all sorts of sources.
Addressing any errors and bugs, like incomplete or duplicate data, and processing the data in a suitable format to solve the research questions.
Algorithms are used to find patterns from data.
Identifying if or how another model’s output will contribute to the achievement of a business objective.
In order to acquire the optimum outcome, an iterative process is frequently used to identify the best method.
Getting the project’s findings suitable for making decisions in real-time

The techniques and actions listed above are repeated until the best outcomes are achieved. Our engineers and developers have extensive knowledge of the tools, techniques, and approaches used in the processes described above. We guarantee that we will provide the best research advice w.r.t to data mining thesis topics and complete your project on schedule. What are the important data mining tasks?

Data Mining Tasks

Data mining finds application in many ways including description, Analysis, summarization of data, and clarifying the conceptual understanding by data description
And also prediction, classification, dependency analysis, segmentation, and case-based reasoning are some of the important data mining tasks
Regression – numerical data prediction (stock prices, temperatures, and total sales)
Data warehousing – business decision making and large-scale data mining
Classification – accurate prediction of target classes and their categorization
Association rule learning – market-based analytical tools that were involved in establishing variable data set relationship
Machine learning – statistical probability-based decision making method without complicated programming
Data analytics – digital data evaluation for business purposes
Clustering – dataset partitioning into clusters and subclasses for analyzing natural data structure and format
Artificial intelligence – human-based Data analytics for reasoning, solving problems, learning, and planning
Data preparation and cleansing – conversion of raw data into a processed form for identification and removal of errors

You can look at our website for a more in-depth look at all of these operations. We supply you with the needed data, as well as any additional data you may need for your data mining thesis topics . We supply non-plagiarized data mining thesis assistance in any fresh idea of your choice. Let us now discuss the stages in data mining that are to be included in your thesis topics

How to work on a data mining thesis topic?

The following are the important stages or phases in developing data mining thesis topics.

First of all, you need to identify the present demand and address the question
The next step is defining or specifying the problem
Collection of data is the third step
Alternative solutions and designs have to be analyzed in the next step
The proposed methodology has to be designed
The system is then to be implemented

Usually, our experts help in writing codes and implementing them successfully without hassles . By consistently following the above steps you can develop one of the best data mining thesis topics of recent days. Furthermore, technically it is important for you to have a better idea of all the tasks and techniques involved in data mining about which we have discussed below

Data visualization
Neural networks
Statistical modeling
Genetic algorithms and neural networks
Decision trees and induction
Discriminant analysis
Induction techniques
Association rules and data visualization
Bayesian networks
Correlation
Regression analysis
Regression analysis and regression trees

If you are looking forward to selecting the best tool for your data mining project then evaluating its consistency and efficiency stands first. For this, you need to gain enough technical data from real-time executed projects for which you can directly contact us. Since we have delivered an ample number of data mining thesis topics successfully we can help you in finding better solutions to all your research issues. What are the points to be remembered about the data mining strategy?

Furthermore, data mining strategies must be picked before instruments in order to prevent using strategies that do not align with the article’s true purposes.
The typical data mining strategy has always been to evaluate a variety of methodologies in order to select one which best fits the situation.
As previously said, there are some principles that may be used to choose effective strategies for data mining projects.
Since they are easy to handle and comprehend
They could indeed collaborate with definitional and parametric data
Tare unaffected by critical values, they could perhaps function with incomplete information
They could also expose various interrelationships and an absence of linear combinations
They could indeed handle noise in records
They can process huge amounts of data.
Decision trees, on the other hand, have significant drawbacks.
Many rules are frequently necessary for dependent variables or numerous regressions, and tiny changes in the data can result in very different tree architectures.

All such pros and cons of various data mining aspects are discussed on our website. We will provide you with high-quality research assistance and thesis writing assistance . You may see proof of our skill and the unique approach that we generated in the field by looking at the samples of the thesis that we produced on our website. We also offer an internal review to help you feel more confident. Let us now discuss the recent data mining methodologies

Current methods in Data Mining

Prediction of data (time series data mining)
Discriminant and cluster analysis
Logistic regression and segmentation

Our technical specialists and technicians usually give adequate accurate data, a thorough and detailed explanation, and technical notes for all of these processes and algorithms. As a result, you can get all of your questions answered in one spot. Our technical team is also well-versed in current trends, allowing us to provide realistic explanations for all new developments. We will now talk about the latest data mining trends

Latest Trending Data Mining Thesis Topics

Visual data mining and data mining software engineering
Interaction and scalability in data mining
Exploring applications of data mining
Biological and visual data mining
Cloud computing and big data integration
Data security and protecting privacy in data mining
Novel methodologies in complex data mining
Data mining in multiple databases and rationalities
Query language standardization in data mining
Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining

These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis . We design the plan in a chronological order relevant to the study assessment with this in mind. The incorporation of citations is one of the most important aspects of the thesis. We focus not only on authoring but also on citing essential sources in the text. Students frequently struggle to deal with appropriate proposals when commencing their thesis. We have years of experience in providing the greatest study and data mining thesis writing services to the scientific community, which are promptly and widely acknowledged. We will now talk about future research directions of research in various data mining thesis topics

Future Research Directions of Data Mining

The potential of data mining and data science seems promising, as the volume of data continues to grow.
It is expected that the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes.
We’ll also generate 1.7 gigabytes of new data for every human being on this planet each second.
Mining algorithms have completely transformed as technology has advanced, and thus have tools for obtaining useful insights from data.
Only corporations like NASA could utilize their powerful computers to examine data once upon a time because the cost of producing and processing data was simply too high.
Organizations are now using cloud-based data warehouses to accomplish any kinds of great activities with machine learning, artificial intelligence, and deep learning.

The Internet of Things as well as wearable electronics, for instance, has transformed devices to be connected into data-generating engines which provide limitless perspectives into people and organizations if firms can gather, store, and analyze the data quickly enough. What are the aspects to be remembered for choosing the best data mining thesis topics?

An excellent thesis topic is a broad concept that has to be developed, verified, or refuted.
Your thesis topic must capture your curiosity, as well as the involvement of both the supervisor and the academicians.
Your thesis topic must be relevant to your studies and should be able to withstand examination.

Our engineers and experts can provide you with any type of research assistance on any of these data mining development tools . We satisfy the criteria of your universities by ensuring several revisions, appropriate formatting and editing of your thesis, comprehensive grammar check, and so on . As a result, you can contact us with confidence for complete assistance with your data mining thesis. What are the important data mining thesis topics?

Trending Data Mining Research Thesis Topics

Research Topics in Data Mining

Handling cost-effective, unbalanced non-static data
Issues related to data mining and their solutions
Network settings in data mining and ensuring privacy, security, and integrity of data
Environmental and biological issues in data mining
Complex data mining and sequential data mining (time series data)
Data mining at higher dimensions
Multi-agent data mining and distributed data mining
High-speed data mining
Development of unified data mining theory

We currently provide full support for all parts of research study, development, investigation, including project planning, technical advice, legitimate scientific data, thesis writing, paper publication, assignments and project planning, internal review, and many other services. As a result, you can contact us for any kind of help with your data mining thesis topics.

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Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

User activity classification and domain-wise ranking through social interactions.

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

A data mining analysis of COVID-19 cases in states of United States of America

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

Exploring distributed energy generation for sustainable development: A data mining approach

A comprehensive guideline for bengali sentiment annotation.

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

The Influence of E-book Teaching on the Motivation and Effectiveness of Learning Law by Using Data Mining Analysis

This paper studies the motivation of learning law, compares the teaching effectiveness of two different teaching methods, e-book teaching and traditional teaching, and analyses the influence of e-book teaching on the effectiveness of law by using big data analysis. From the perspective of law student psychology, e-book teaching can attract students' attention, stimulate students' interest in learning, deepen knowledge impression while learning, expand knowledge, and ultimately improve the performance of practical assessment. With a small sample size, there may be some deficiencies in the research results' representativeness. To stimulate the learning motivation of law as well as some other theoretical disciplines in colleges and universities has particular referential significance and provides ideas for the reform of teaching mode at colleges and universities. This paper uses a decision tree algorithm in data mining for the analysis and finds out the influencing factors of law students' learning motivation and effectiveness in the learning process from students' perspective.

Intelligent Data Mining based Method for Efficient English Teaching and Cultural Analysis

The emergence of online education helps improving the traditional English teaching quality greatly. However, it only moves the teaching process from offline to online, which does not really change the essence of traditional English teaching. In this work, we mainly study an intelligent English teaching method to further improve the quality of English teaching. Specifically, the random forest is firstly used to analyze and excavate the grammatical and syntactic features of the English text. Then, the decision tree based method is proposed to make a prediction about the English text in terms of its grammar or syntax issues. The evaluation results indicate that the proposed method can effectively improve the accuracy of English grammar or syntax recognition.

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Data Mining Dissertation Topics

The term “data mining” refers to an intelligent data lookup capacity that uses statistics-based algorithms and methodologies to find trends, patterns, links, and correlations within the collected data and records. Audio, Pictorial, Video, textual, online, and social media-based mining are only a few examples of data mining. This article will provide you with a complete overview of various recent data mining dissertation topics . Let us first start with the definition of data mining processes.

What is the data mining process?

The practice of evaluating a huge batch containing data to find different patterns is known as data mining.
Companies can utilize data mining for a variety of purposes, including knowing as to what consumers are engaged in or would like to buy, as well as detection of fraudulent activities and malware scanning.

Hence data mining plays a very significant role in both commercial and personal life aspects of the modern world. We have been working on data mining dissertation topics and project ideas for more than 15 years as a result of which we have gained huge expertise and have acquired vast knowledge, skills, and experience in the field. So we can guide you in all the existing and normal data mining methods and techniques. Let us now talk about the data mining techniques below

Data mining techniques

Neural networks
Rule induction
Nearest neighbor classification
Decision tree
Descriptive techniques – sequential analysis, association, and clustering

Complete explanation and description on all these techniques and methods are available at our website on data mining dissertation topics . By understanding the importance of data mining, we have successfully worked out several advanced projects and implementations in real-time . Check out our website for all details about our successful projects in data mining. Let us now see about the data mining approaches below

Approaches in data mining

Belief nets
Neural nets (Kohonen and backpropagation)
Decision trees (CHAID, CAITT, and C 4.5)
Rules (genetic algorithms and induction)
Case-based reasoning
Nearest neighbor

This is the basic classification of the various data mining approaches that are in use today. With the support of the best engineers and world-class certified experts in data mining , we are here to provide you with a massive amount of reliable and authentic research data along with complete support in interpretation, analysis, and understanding them . Get in touch with us at any time for complete support for your data mining dissertation . We assure to give you full support and ultimate guidance on any data mining dissertation topics. We will now talk about the major issues in data mining

Major issues in data mining

Parallel, distributed, and incremental mining algorithms
Data mining algorithm efficiency and scalability
Incorporation of background data
Interactive meaning
Data mining result presentation and visualization
Pattern evaluation meaning
pattern and Constraint guided mining
Power boosting in networking environment
Data mining interdisciplinary approach
Data insufficiency and uncertainty
Handling the issues of noise
Multidimensional data mining space
Novel approaches and incorporating multiple aspects of data mining

We have handled all these issues efficiently and have devised successful methods to overcome them. Get in touch with us to know more about the potential data mining solutions and advanced techniques used in overcoming the issues of data mining . What are the top data mining topics?

Data Mining Tools

WEKA, Orange, Tanagra and NLTK
Angoss, Oracle, and STATISTICA (or StatSoft)
Pentaho, Rattle, and Apache Mahout
RapidMiner, R – programming, and KNIME
JHepWork, IBM SPSS, and SAS Enterprise Miner

The tips and advice in using these tools of data mining are explained in detail on our website. Also, we are here to help you in handling these data mining tools efficiently with proper demonstrations and explanations. Our engineers have great skills in working with these data mining tools. So reach out to us for any support related to data mining. What are the recent trends in data mining?

Latest trends in data mining

Spatial data mining and semantic web mining
Personalized systems for recommendations and low-quality source data mining
Data retrieval based on content and multimedia retrieval
Graph theory data retrieval and data mining quantum computing
Integration of data warehousing and DNA
Retrieval based on content and audio mining at low quality
Itemset mining for optimization of MapReduce
Analyzing sentiments on social media and P2P
Assessing the quality of multimedia and Internet of Things applications using data mining
Management based on grid databases and Context-aware computing

At present we are offering complete project support and dissertation writing guidance along with assignments, paper publication, proposal, thesis, and many more with proper grammatical checks, full review, and approval. Therefore we are here to help you in all aspects of your data mining research . What are the Datasets available for data mining?

Datasets for Data Mining Projects

It is a data marketplace and open catalog
With infochimps, you shall perform sharing, selling, curative, and data downloading
It has blogs of about forty-four million
It ranges from August to October of 2008
Artificial intelligence-based photos and data collection
Useful for academic and research purposes
Collection of geospatial and geographic data
Artificial intelligence and machine learning-based updated data collection
Data is collected from around ten thousand Europe based companies
It is a repository of molecular abundance and gene expression
It supports MIAME compliances
Retrieving, querying, and browsing data is made possible with this gene expression resource
Collection of stocks and futures-based financial data
Google-based text collection from various books

Apart from these relevant datasets, there are also many other datasets including CIDDS, DAPARA, CICIDS2017, ADFA – IDS, TUIDS, ISCXIDS2012, AWID, and NSL – KDD . Complete information on all these datasets and tips for handling them efficiently will be shared with you as you avail of our services on data mining dissertation topics . Feel free to interact with our experts regarding any doubts in your data mining research. We ensure to solve all your doubts instantly.

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Home » Blog » Dissertation » Topics » Information Technology » 80 Data Mining Research Topics

80 Data Mining Research Topics

FacebookXEmailWhatsAppRedditPinterestLinkedInAre you a student embarking on a research journey in the field of data mining, searching for that perfect set of research topics to catalyze your undergraduate, master’s, or doctoral thesis or dissertation? Well, you’ve arrived at the right place! The world of data mining is a captivating realm that offers a plethora of opportunities […]

Are you a student embarking on a research journey in the field of data mining, searching for that perfect set of research topics to catalyze your undergraduate, master’s, or doctoral thesis or dissertation? Well, you’ve arrived at the right place! The world of data mining is a captivating realm that offers a plethora of opportunities for exploration and innovation. Your research journey begins with the careful selection of research topics, a dec All Posts ision that will set the course for your academic pursuits. In this comprehensive guide, we will delve into an array of intriguing data mining research topics, spanning various complexity levels and domains, to help you navigate the fascinating landscape of data-driven discovery.

Data Mining, often referred to as”data analysis,” “data exploration,” “pattern recognition,” and “predictive modeling”, is the process of extracting valuable patterns, insights, and knowledge from large datasets.

A List Of Potential Research Topics In Data Mining:

Assessing the impact of data mining in UK law enforcement for crime prevention.
Analyzing the use of data mining in predicting and mitigating the impact of Brexit on UK businesses.
A review of data mining in personalized education and adaptive learning systems.
A comprehensive analysis of data mining for image and video analysis in computer vision.
Assessing the role of data mining in identifying and mitigating insider threats in organizations.
A systematic review of data mining applications in the healthcare industry.
Investigating the effectiveness of data mining in predicting stock market trends.
A review of data mining approaches for text and sentiment analysis.
A review of data mining applications in the automotive industry for predictive maintenance.
Evaluating the impact of the COVID-19 pandemic on data mining techniques in healthcare analytics.
Exploring the application of data mining in optimizing energy consumption in smart homes.
A critical examination of data mining methods for social network analysis and community detection.
A critical assessment of data mining tools and software for beginners.
Examining the use of data mining in optimizing resource allocation in cloud computing.
Analyzing the application of data mining in improving personalized healthcare recommendations.
Investigating the use of data mining in natural language processing for sentiment analysis.
Investigating the application of data mining in predicting and preventing cyberattacks.
Investigating the challenges and opportunities of data mining in UK higher education institutions.
Investigating the effectiveness of data mining techniques in identifying rare events in medical data.
Analyzing the use of data mining in optimizing manufacturing processes for quality control.
Assessing the use of data mining in predicting urban traffic congestion.
Data mining techniques for detecting cybersecurity threats.
A systematic review of data mining approaches for predicting customer churn in telecommunications.
Investigating the use of data mining in cybersecurity for threat detection and prevention.
Exploring the application of data mining in predicting disease outbreaks using epidemiological data.
Exploring the role of data mining in credit scoring and risk assessment for lending institutions.
Analyzing the application of data mining in enhancing energy efficiency in UK homes.
Assessing the role of data mining in remote monitoring and telehealth during and post-COVID-19.
Analyzing the use of data mining in tracking and predicting the spread of infectious diseases.
Assessing the application of data mining in optimizing agricultural practices for crop yield prediction.
Analyzing the impact of data mining in improving personalized healthcare interventions.
Assessing the role of data mining in personalized recommendation systems for e-commerce.
An assessment of data mining in optimizing energy consumption in smart cities.
Exploring the application of data mining in optimizing public transportation systems in the UK.
A comparative analysis of data mining techniques for fraud detection in the banking sector.
Investigating the role of data mining in analyzing social media data for political campaigns in the UK.
Investigating the use of data mining in optimizing inventory management for e-commerce businesses.
A comprehensive review of data preprocessing techniques in data mining.
Exploring the ethical implications of data mining in online privacy and data protection.
Analyzing the impact of data mining in sentiment analysis of customer reviews in the hospitality industry.
Assessing the impact of data mining in analyzing social network data for marketing strategies.
Exploring the role of data mining in enhancing recommendation systems for online learning platforms.
Analyzing the ethical implications of data mining in the collection and use of personal data.
Investigating the impact of deep learning techniques on sentiment analysis in social media data.
Investigating the role of data mining in identifying patterns of criminal behavior for law enforcement.
An in-depth review of data mining techniques for anomaly detection in cybersecurity.
A critical review of data mining algorithms for imbalanced datasets.
Exploring the role of data mining in analyzing cultural trends and social behaviors.
Analyzing the application of data mining in recommendation systems for streaming platforms.
Leveraging artificial intelligenc e in data mining for enhanced insights.
Examining the ethical implications of data mining in public policy decision-making.
Assessing the effectiveness of data mining in predicting customer churn in telecommunications.
Investigating the challenges and opportunities of data mining in analyzing pandemic-related data.
Investigating the challenges and opportunities of data mining in educational data analytics.
A review of data mining applications in environmental science and climate modeling.
Assessing the effectiveness of clustering algorithms in customer segmentation for marketing.
Investigating the challenges and opportunities of data mining in analyzing geospatial data.
A review of data mining algorithms for recommendation systems in e-commerce.
Analyzing the effectiveness of data mining in addressing climate change challenges in the UK.
Assessing the effectiveness of data mining techniques in early detection of diseases from medical images.
A comparative review of clustering algorithms in data mining.
Examining the use of data mining in improving personalized healthcare services in the UK.
A comprehensive review of data mining in the context of big data analytics.
Evaluating the adoption and impact of data mining in the UK’s National Health Service (NHS).
A survey of data mining applications in the financial sector.
Assessing the impact of COVID-19 on data privacy and ethical considerations in data mining.
Analyzing the impact of data mining in predicting disease outbreaks in developing countries.
Examining the use of data mining in analyzing vaccine distribution and uptake data.
Assessing the role of data mining in analyzing COVID-19 data for policy decisions in the UK.
Investigating the ethical considerations in data mining for personalized advertising.
Assessing the impact of data mining on sentiment analysis in political discourse.
Analyzing the use of data mining in optimizing energy consumption in smart grid systems.
Exploring the role of data mining in predicting student academic performance in online education.
Analyzing the effectiveness of data mining in understanding changes in consumer behavior during the pandemic.
Examining the impact of data preprocessing techniques on the performance of classification models.
Assessing the ethical considerations in data mining for social media content analysis.
Analyzing the utilization of blockchain technology for secure and transparent data sharing in healthcare.
Examining the effectiveness of data mining techniques in analyzing environmental data for climate studies.
An extensive review of data mining techniques for time-series data analysis.
Investigating the role of data mining in predicting and preventing traffic accidents.

In the exhilarating world of data mining research, the possibilities are as limitless as the data itself. Whether you’re pursuing an undergraduate, master’s, or doctoral degree, we’ve provided you with a diverse spectrum of research topics to ignite your academic journey. Now equipped with this list of thought-provoking themes, it’s time to dive headfirst into the depths of data mining, where research, topics, and innovation converge to shape the future of data-driven discovery. Happy exploring!

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Data Mining

Data Science
Data and Artificial Intelligence

Student theses

1 - 50 out of 258 results
Title (descending)

Search results

3d face reconstruction using deep learning.

Supervisor: Medeiros de Carvalho, R. (Supervisor 1), Gallucci, A. (Supervisor 2) & Vanschoren, J. (Supervisor 2)

Student thesis : Master

Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making

Supervisor: Pechenizkiy, M. (Supervisor 1), Gajane, P. (Supervisor 2) & Kapodistria, S. (Supervisor 2)

Activity Recognition Using Deep Learning in Videos under Clinical Setting

Supervisor: Duivesteijn, W. (Supervisor 1), Papapetrou, O. (Supervisor 2), Zhang, L. (External person) (External coach) & Vasu, J. D. (External coach)

A Data Cleaning Assistant

Supervisor: Vanschoren, J. (Supervisor 1)

Student thesis : Bachelor

A Data Cleaning Assistant for Machine Learning

A deep learning approach for clustering a multi-class dataset.

Supervisor: Pei, Y. (Supervisor 1), Marczak, M. (External person) (External coach) & Groen, J. (External person) (External coach)

Aerial Imagery Pixel-level Segmentation

A framework for understanding business process remaining time predictions.

Supervisor: Pechenizkiy, M. (Supervisor 1) & Scheepens, R. J. (Supervisor 2)

A Hybrid Model for Pedestrian Motion Prediction

Supervisor: Pechenizkiy, M. (Supervisor 1), Muñoz Sánchez, M. (Supervisor 2), Silvas, E. (External coach) & Smit, R. M. B. (External coach)

Algorithms for center-based trajectory clustering

Supervisor: Buchin, K. (Supervisor 1) & Driemel, A. (Supervisor 2)

Allocation Decision-Making in Service Supply Chain with Deep Reinforcement Learning

Supervisor: Zhang, Y. (Supervisor 1), van Jaarsveld, W. L. (Supervisor 2), Menkovski, V. (Supervisor 2) & Lamghari-Idrissi, D. (Supervisor 2)

Analyzing Policy Gradient approaches towards Rapid Policy Transfer

An empirical study on dynamic curriculum learning in information retrieval.

Supervisor: Fang, M. (Supervisor 1)

An Explainable Approach to Multi-contextual Fake News Detection

Supervisor: Pechenizkiy, M. (Supervisor 1), Pei, Y. (Supervisor 2) & Das, B. (External person) (External coach)

An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks

Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach)

Anomaly detection in image data sets using disentangled representations

Supervisor: Menkovski, V. (Supervisor 1) & Tonnaer, L. M. A. (Supervisor 2)

Anomaly Detection in Polysomnography signals using AI

Supervisor: Pechenizkiy, M. (Supervisor 1), Schwanz Dias, S. (Supervisor 2) & Belur Nagaraj, S. (External person) (External coach)

Anomaly detection in text data using deep generative models

Supervisor: Menkovski, V. (Supervisor 1) & van Ipenburg, W. (External person) (External coach)

Anomaly Detection on Dynamic Graph

Supervisor: Pei, Y. (Supervisor 1), Fang, M. (Supervisor 2) & Monemizadeh, M. (Supervisor 2)

Anomaly Detection on Finite Multivariate Time Series from Semi-Automated Screwing Applications

Supervisor: Pechenizkiy, M. (Supervisor 1) & Schwanz Dias, S. (Supervisor 2)

Anomaly Detection on Multivariate Time Series Using GANs

Supervisor: Pei, Y. (Supervisor 1) & Kruizinga, P. (External person) (External coach)

Anomaly detection on vibration data

Supervisor: Hess, S. (Supervisor 1), Pechenizkiy, M. (Supervisor 2), Yakovets, N. (Supervisor 2) & Uusitalo, J. (External person) (External coach)

Application of P&ID symbol detection and classification for generation of material take-off documents (MTOs)

Supervisor: Pechenizkiy, M. (Supervisor 1), Banotra, R. (External person) (External coach) & Ya-alimadad, M. (External person) (External coach)

Applications of deep generative models to Tokamak Nuclear Fusion

Supervisor: Koelman, J. M. V. A. (Supervisor 1), Menkovski, V. (Supervisor 2), Citrin, J. (Supervisor 2) & van de Plassche, K. L. (External coach)

A Similarity Based Meta-Learning Approach to Building Pipeline Portfolios for Automated Machine Learning

Aspect-based few-shot learning.

Supervisor: Menkovski, V. (Supervisor 1)

Assessing Bias and Fairness in Machine Learning through a Causal Lens

Supervisor: Pechenizkiy, M. (Supervisor 1)

Assessing fairness in anomaly detection: A framework for developing a context-aware fairness tool to assess rule-based models

Supervisor: Pechenizkiy, M. (Supervisor 1), Weerts, H. J. P. (Supervisor 2), van Ipenburg, W. (External person) (External coach) & Veldsink, J. W. (External person) (External coach)

A Study of an Open-Ended Strategy for Learning Complex Locomotion Skills

A systematic determination of metrics for classification tasks in openml, a universally applicable emm framework.

Supervisor: Duivesteijn, W. (Supervisor 1), van Dongen, B. F. (Supervisor 2) & Yakovets, N. (Supervisor 2)

Automated machine learning with gradient boosting and meta-learning

Automated object recognition of solar panels in aerial photographs: a case study in the liander service area.

Supervisor: Pechenizkiy, M. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Weelinck, T. (External person) (External coach)

Automatic data cleaning

Automatic scoring of short open-ended questions.

Supervisor: Pechenizkiy, M. (Supervisor 1) & van Gils, S. (External coach)

Automatic Synthesis of Machine Learning Pipelines consisting of Pre-Trained Models for Multimodal Data

Automating string encoding in automl, autoregressive neural networks to model electroencephalograpy signals.

Supervisor: Vanschoren, J. (Supervisor 1), Pfundtner, S. (External person) (External coach) & Radha, M. (External coach)

Balancing Efficiency and Fairness on Ride-Hailing Platforms via Reinforcement Learning

Supervisor: Tavakol, M. (Supervisor 1), Pechenizkiy, M. (Supervisor 2) & Boon, M. A. A. (Supervisor 2)

Benchmarking Audio DeepFake Detection

Better clustering evaluation for the openml evaluation engine.

Supervisor: Vanschoren, J. (Supervisor 1), Gijsbers, P. (Supervisor 2) & Singh, P. (Supervisor 2)

Bi-level pipeline optimization for scalable AutoML

Supervisor: Nobile, M. (Supervisor 1), Vanschoren, J. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Bliek, L. (Supervisor 2)

Block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks

Supervisor: Mocanu, D. (Supervisor 1), Zhang, Y. (Supervisor 2) & Lowet, D. J. C. (External coach)

Boolean Matrix Factorization and Completion

Supervisor: Peharz, R. (Supervisor 1) & Hess, S. (Supervisor 2)

Bootstrap Hypothesis Tests for Evaluating Subgroup Descriptions in Exceptional Model Mining

Supervisor: Duivesteijn, W. (Supervisor 1) & Schouten, R. M. (Supervisor 2)

Bottom-Up Search: A Distance-Based Search Strategy for Supervised Local Pattern Mining on Multi-Dimensional Target Spaces

Supervisor: Duivesteijn, W. (Supervisor 1), Serebrenik, A. (Supervisor 2) & Kromwijk, T. J. (Supervisor 2)

Bridging the Domain-Gap in Computer Vision Tasks

Supervisor: Mocanu, D. C. (Supervisor 1) & Lowet, D. J. C. (External coach)

CCESO: Auditing AI Fairness By Comparing Counterfactual Explanations of Similar Objects

Supervisor: Pechenizkiy, M. (Supervisor 1) & Hoogland, K. (External person) (External coach)

Clean-Label Poison Attacks on Machine Learning

Supervisor: Michiels, W. P. A. J. (Supervisor 1), Schalij, F. D. (External coach) & Hess, S. (Supervisor 2)

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Data Analytics and Machine Learning Group
TUM School of Computation, Information and Technology
Technical University of Munich

Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A non-exhaustive list of open topics is listed below.

If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential topics.

Robustness of Large Language Models

Type: Master's Thesis

Prerequisites:

Strong knowledge in machine learning
Very good coding skills
Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)
Knowledge about NLP and LLMs

Description:

The success of Large Language Models (LLMs) has precipitated their deployment across a diverse range of applications. With the integration of plugins enhancing their capabilities, it becomes imperative to ensure that the governing rules of these LLMs are foolproof and immune to circumvention. Recent studies have exposed significant vulnerabilities inherent to these models, underlining an urgent need for more rigorous research to fortify their resilience and reliability. A focus in this work will be the understanding of the working mechanisms of these attacks.

We are currently seeking students for the upcoming Summer Semester of 2024, so we welcome prompt applications. This project is in collaboration with Google Research .

Contact: Tom Wollschläger

References:

Universal and Transferable Adversarial Attacks on Aligned Language Models
Attacking Large Language Models with Projected Gradient Descent
Representation Engineering: A Top-Down Approach to AI Transparency
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks

Generative Models for Drug Discovery

Type: Mater Thesis / Guided Research

Strong machine learning knowledge
Proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
Knowledge of graph neural networks (e.g. GCN, MPNN)
No formal education in chemistry, physics or biology needed!

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain which has experienced great attention through the success of generative models. These models promise a more efficient exploration of the vast chemical space and generation of novel compounds with specific properties by leveraging their learned representations, potentially leading to the discovery of molecules with unique properties that would otherwise go undiscovered. Our topics lie at the intersection of generative models like diffusion/flow matching models and graph representation learning, e.g., graph neural networks. The focus of our projects can be model development with an emphasis on downstream tasks ( e.g., diffusion guidance at inference time ) and a better understanding of the limitations of existing models.

Contact : Johanna Sommer , Leon Hetzel

Equivariant Diffusion for Molecule Generation in 3D

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Structure-based Drug Design with Equivariant Diffusion Models

Efficient Machine Learning: Pruning, Quantization, Distillation, and More - DAML x Pruna AI

Type: Master's Thesis / Guided Research / Hiwi

The efficiency of machine learning algorithms is commonly evaluated by looking at target performance, speed and memory footprint metrics. Reduce the costs associated to these metrics is of primary importance for real-world applications with limited ressources (e.g. embedded systems, real-time predictions). In this project, you will work in collaboration with the DAML research group and the Pruna AI startup on investigating solutions to improve the efficiency of machine leanring models by looking at multiple techniques like pruning, quantization, distillation, and more.

Contact: Bertrand Charpentier

The Efficiency Misnomer
A Gradient Flow Framework for Analyzing Network Pruning
Distilling the Knowledge in a Neural Network
A Survey of Quantization Methods for Efficient Neural Network Inference

Deep Generative Models

Type: Master Thesis / Guided Research

Strong machine learning and probability theory knowledge
Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

Flow Matching for Generative Modeling
Auto-Encoding Variational Bayes
Denoising Diffusion Probabilistic Models
Structured Denoising Diffusion Models in Discrete State-Spaces

Graph Structure Learning

Type: Guided Research / Hiwi

Optional: Knowledge of graph theory and mathematical optimization

Graph deep learning is a powerful ML concept that enables the generalisation of successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results in a vast range of applications spanning the social sciences, biomedicine, particle physics, computer vision, graphics and chemistry. One of the major limitations of most current graph neural network architectures is that they often rely on the assumption that the underlying graph is known and fixed. However, this assumption is not always true, as the graph may be noisy or partially and even completely unknown. In the case of noisy or partially available graphs, it would be useful to jointly learn an optimised graph structure and the corresponding graph representations for the downstream task. On the other hand, when the graph is completely absent, it would be useful to infer it directly from the data. This is particularly interesting in inductive settings where some of the nodes were not present at training time. Furthermore, learning a graph can become an end in itself, as the inferred structure can provide complementary insights with respect to the downstream task. In this project, we aim to investigate solutions and devise new methods to construct an optimal graph structure based on the available (unstructured) data.

Contact : Filippo Guerranti

A Survey on Graph Structure Learning: Progress and Opportunities
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Learning Discrete Structures for Graph Neural Networks

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

A Machine Learning Perspective on Corner Cases in Autonomous Driving Perception

Type: Master's Thesis

Industrial partner: BMW

Prerequisites:

Strong knowledge in machine learning
Knowledge of Semantic Segmentation
Good programming skills
Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)

Description:

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example semantic segmentation. While the environment in datasets is controlled in real world application novel class or unknown disturbances can occur. To provide safe autonomous driving these cased must be identified.

The objective is to explore novel class segmentation and out of distribution approaches for semantic segmentation in the context of corner cases for autonomous driving.

Contact: Sebastian Schmidt

References:

Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
Efficient Uncertainty Estimation for Semantic Segmentation in Videos
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family
Description of Corner Cases in Automated Driving: Goals and Challenges

Active Learning for Multi Agent 3D Object Detection

Type: Master's Thesis Industrial partner: BMW

Knowledge in Object Detection
Excellent programming skills

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.

Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
Efficient Uncertainty Estimation for Semantic Segmentation in Videos
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Towards Open World Active Learning for 3D Object Detection

Graph Neural Networks

Type: Master's thesis / Bachelor's thesis / guided research

Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

Semi-supervised classification with graph convolutional networks
Relational inductive biases, deep learning, and graph networks
Diffusion Improves Graph Learning
Weisfeiler and leman go neural: Higher-order graph neural networks
Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type: Master's thesis / guided research

Proficiency with Python and deep learning frameworks (JAX or PyTorch)
Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

Directional Message Passing for Molecular Graphs
Neural message passing for quantum chemistry
Learning to Simulate Complex Physics with Graph Network
Ab initio solution of the many-electron Schrödinger equation with deep neural networks
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
Strong background in mathematical optimization (preferably combined with Machine Learning setting)
Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
(Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

Intriguing properties of neural networks
Explaining and harnessing adversarial examples
SoK: Certified Robustness for Deep Neural Networks
Certified Adversarial Robustness via Randomized Smoothing
Formal guarantees on the robustness of a classifier against adversarial manipulation
Towards deep learning models resistant to adversarial attacks
Provable defenses against adversarial examples via the convex outer adversarial polytope
Certified defenses against adversarial examples
Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger , Dominik Fuchsgruber , Bertrand Charpentier

Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Predictive Uncertainty Estimation via Prior Networks
Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
Evidential Deep Learning to Quantify Classification Uncertainty
Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type: Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
Hierarchical Graph Representation Learning with Differentiable Pooling
Gradient-based Hierarchical Clustering
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

Thesis Helpers

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Trendy Big Data Research Topics

Students that want to focus on emerging issues when writing academic papers and essays should choose trendy data science topics. Big data covers the initiatives and technologies that tackle massive and diverse data when it comes to addressing traditional skills, technologies, and infrastructure efficiently. Here are some of the latest data topics to consider when writing a research paper or essay.

Tools and software for processing big data
Privacy and security issues that face big data
Scalable architectures for processing massively parallel data
Analyzing large scale data for social networks
Scalable big data storage systems
Platforms for big data computing- Big data analytics and adoption
How to analyze big data
How to effectively manage big data
Parallel big data programming and processing techniques
Semantics in big data
Visualization of big data
Business intelligence and big data analytics
Map-reduce architecture and Hadoop programming
Methods for machine learning in big data
Big data analytics and privacy preservation
How to process stream data in big data
Uncertainty in big data management
Anomaly detection in large scale data systems
Analytics for big data in the Smart Healthcare systems
The importance of big data technologies for modern businesses

These are great data research topics that learners at different study levels should consider when asked to write academic papers or essays. However, extensive research is required to come up with great write-ups on these topics.

Data Mining Research Topics for Students

Data mining refers to the extraction of useful information from raw data. It’s a technique that companies apply to accomplish tasks like prediction analysis, generation of the association rule, and clustering. Data mining topics can explain this technique or address issues that are associated with it. Here are some of the best data mining project topics that learners can consider.

Big data mining techniques and tools
Model-based clustering of texts
Describe the concept of data spectroscopic clustering
Parallel spectral clustering within a distributed system
Describe asymmetrical spectral clustering
What is information-based clustering?
Self-turning spectral clustering
Symmetrical spectral clustering
Discuss the K-Means algorithms in data clustering
Discuss the package of MATLAB spectral clustering
Discuss the K-Means clustering from an online spherical perspective
Discuss the hierarchical clustering application
Explain the importance of probabilistic classification in data mining
How can the effectiveness of nonlinear and linear regression analysis be improved?
Explain the Association Rule Learning regarding data mining
Explain the performance of dependency modeling
Discuss the performance of representative-based clustering
Explain the need for density-based clustering
Discuss the importance of subject-based data mining when it comes to reducing terrorism
How can data mining be used to analyze transaction data in a supermarket?

Most data mining current research topics focus on finding or establishing patterns. Students can even find some of the best data mining case study topics in this category. Nevertheless, every idea requires detailed and extensive research to come up with facts that make a great paper or essay.

Big Data Analysis Topics

The moderns IT industry depends on data analytics as its lifeline. Big data is one of the techniques and technologies that are used to analyze vast data volumes. The industry is using data analytics as a strategy for gaining insights into system performance and customer behavior. Here are some of the best data analytics research topics that students can consider when writing academic papers.

Internet of Things
Describe the importance of augmented reality
How important is artificial intelligence?
Explain the graph analytics process
What is agile data science?
Why is machine intelligence for modern businesses?
What is hyper-personalization?
Explain the behavioral analytics process
What is the experience economy?
Discuss journey sciences
Discuss knowledge validation and extraction
What is semantic data management?
Explain the deep learning process
Explain software engineering for big data science
What is structured machine learning?
Explain semantic question answering
What is distributed semantic analytics?
Why is domain knowledge important in data analysis?
Why is data exploration important in data analysis?
Who uses big data analytics?

Writing about data analytics topics requires background knowledge of the issues being discussed. That’s because the analysis entails harnessing data and extracting its value.

Data Management Project Topics

This category has some of the best data science research topics. The enormous amount of data that modern organizations have to deal with every day is not easy to handle. As such, its effective management is required to ensure its effective use. Here are some of the best topics that students can write about in this aspect.

Describe some of the most innovative bid data management concepts
Data catalogs: Describe approaches and their implementation, as well as, adoption
How to manage platforms for enterprise analytics
Discuss the impact of data quality on a business
Explain the best data management strategies for modern enterprises
New technologies and AI in data management
What is data retention and why is it important?
Describe the basics of data management
Explain the application of data management basics
Data publishing and access by modern companies
Explain the process of analyzing and managing data for reproducible research
Explain how to work with images during research
How can an organization ensure secure and confidential handling and management of data?
How to promote research and scientific outreach through data management
How to source and manage external data
How to ensure effective data protection through proper management
Data catalog reference model and market study
What is data valuation and why does it matter in data management?
How can machine learning improve the data quality?
How can a company implement data governance?

This category also has some of the best big data seminar topics. That’s because some of the ideas featured in this section are about issues that affect almost every organization.

Resent Data Security Topics for Research

Big data that comes from different computers and devices require security. That’s because such data is vulnerable to different cyber threats. Some of the best research topics in this category include the following.

How changing data from Terabytes to Petabytes affects its security
What are the major vulnerabilities for big data?
Why big data owners should update security measures regularly
How can poor data security lead to loss of important information
Describe security technologies that can be used to protect big data
Explain how Hadoop integrates with modern security tools
Which are the best encryption tools for protecting transit data?
Explain how data encryption tools work
What is token-based authentication?
Explain how intrusion prevention and detection systems work
What are the most effective physical systems for securing data?
Which is the best intrusion detection system?
Describe the most suitable key management system when it comes to processing massive data
Which tool or algorithm can be used for data owner and user’s authentication?
Explain how you can determine the amount of secure data
How to identify a legit data user
How to prevent illegitimate data access
How to implement attribute-access or role-based access control
Explain the importance of centralized key management
Why is user-access control important?

Any topic in this category can be used to write a brilliant paper or essay that will earn the learner the top grade. However, time and efforts are required to work on these ideas.

Whether students opt to write about data visualization topics or data structure research topics, the most important thing is to choose ideas they like and find interesting. What’s more, learners should pick topics they can find adequate information for online. That way, they will find the research and writing process enjoyable. They can also buy dissertations or any other academic papers that will impress educators to award them the top grades.

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Dissertations / Theses on the topic 'Data mining – Research'

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Consult the top 50 dissertations / theses for your research on the topic 'Data mining – Research.'

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Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.

Shioda, Romy 1977. "Integer optimization in data mining." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17579.

Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.

Kardell, Oliver [Verfasser]. "DIA data mining in colorectal cancer research / Oliver Kardell." Hamburg : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2020. http://d-nb.info/1223621022/34.

Fang, Yao-chuen. "Scientific research impact and data mining applications in hydrogeology." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1092774125.

Shao, Huijuan. "Temporal Mining Approaches for Smart Buildings Research." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/84349.

Geltz, Rebecca L. "Using Data Mining to Model Student Success." Youngstown State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709.

Flietstra, Bryan C. "A data mining approach for acoustic diagnosis of cardiopulmonary disease." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45400.

Lavelle, Stephen J. "Fabricating synthetic data in support of training for domestic terrorist activity data mining research." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5196.

Snyder, Ashley M. (Ashley Marie). "Data mining and visualization : real time predictions and pattern discovery in hospital emergency rooms and immigration data." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61199.

Knoetze, Ronald Morgan. "The mining and visualisation of application services data." Thesis, Nelson Mandela Metropolitan University, 2005. http://hdl.handle.net/10948/451.

Houston, Andrea L., Hsinchun Chen, Susan M. Hubbard, Bruce R. Schatz, Tobun Dorbin Ng, Robin R. Sewell, and Kristin M. Tolle. "Medical Data Mining on the Internet: Research on a Cancer Information System." Kluwer, 1999. http://hdl.handle.net/10150/106388.

Wu, Qionglin 1964. "Data mining and knowledge discovery in financial research : empirical investigations into currency." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31560.

Burley, Keith Martin. "Data mining techniques in higher education research : the example of student retention." Thesis, Sheffield Hallam University, 2006. http://shura.shu.ac.uk/19412/.

Yuan, Fan. "Modeling and computational strategies for medical decision making." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54857.

Liu, Yang. "Data mining methods for single nucleotide polymorphisms analysis in computational biology." HKBU Institutional Repository, 2011. http://repository.hkbu.edu.hk/etd_ra/1287.

Gadaleta, Emanuela. "A multidisciplinary computational approach to model cancer-omics data : organising, integrating and mining multiple sources of data." Thesis, Queen Mary, University of London, 2015. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8141.

Domm, Maryanne. "Mathematical programming in data mining: Models for binary classification with application to collusion detection in online gambling." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280270.

Pafilis, Evangelos. "Web-based named entity recognition and data integration to accelerate molecular biology research." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:16-opus-89706.

Kamenieva, Iryna. "Research Ontology Data Models for Data and Metadata Exchange Repository." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-6351.

For researches in the field of the data mining and machine learning the necessary condition is an availability of various input data set. Now researchers create the databases of such sets. Examples of the following systems are: The UCI Machine Learning Repository, Data Envelopment Analysis Dataset Repository, XMLData Repository, Frequent Itemset Mining Dataset Repository. Along with above specified statistical repositories, the whole pleiad from simple filestores to specialized repositories can be used by researchers during solution of applied tasks, researches of own algorithms and scientific problems. It would seem, a single complexity for the user will be search and direct understanding of structure of so separated storages of the information. However detailed research of such repositories leads us to comprehension of deeper problems existing in usage of data. In particular a complete mismatch and rigidity of data files structure with SDMX - Statistical Data and Metadata Exchange - standard and structure used by many European organizations, impossibility of preliminary data origination to the concrete applied task, lack of data usage history for those or other scientific and applied tasks.

Now there are lots of methods of data miming, as well as quantities of data stored in various repositories. In repositories there are no methods of DM (data miming) and moreover, methods are not linked to application areas. An essential problem is subject domain link (problem domain), methods of DM and datasets for an appropriate method. Therefore in this work we consider the building problem of ontological models of DM methods, interaction description of methods of data corresponding to them from repositories and intelligent agents allowing the statistical repository user to choose the appropriate method and data corresponding to the solved task. In this work the system structure is offered, the intelligent search agent on ontological model of DM methods considering the personal inquiries of the user is realized.

For implementation of an intelligent data and metadata exchange repository the agent oriented approach has been selected. The model uses the service oriented architecture. Here is used the cross platform programming language Java, multi-agent platform Jadex, database server Oracle Spatial 10g, and also the development environment for ontological models - Protégé Version 3.4.

Chiang, H.-Y., and 蔣筱雲. "A Research of Compensating Missing Data by Data Mining." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/42484926395418255313.

Chou-ChengChen and 陳疇丞. "Application of text mining and data mining in cancer research." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5m9x87.

Cahng, Feng-Hao, and 張峰豪. "Research on machine utilization using data mining." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/9w95bv.

Waranashiwar, Shruti Dilip. "Interactive pattern mining of neuroscience data." Thesis, 2014. http://hdl.handle.net/1805/3878.

Liu, Ying-Ching, and 劉應慶. "The Initial Research of Using Data Mining Techniques for Data Classification Optimization." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/07565943030329965805.

Wen, Hao Liao, and 廖文豪. "The Research of Value Analysis Applying Data Mining Technique." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/10327866038049761206.

Tai, Chuntien, and 戴俊典. "A Research of Data Mining in Warfarin Dosage Decisions." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/12554411984456215497.

Wang, Dung-Chi, and 王東祈. "A Research on Mining Frequent Itemsets in Data Stream." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/4vne38.

LEE, YUN-FANG, and 李雲芳. "Research on Functional Clothing Recommendations by Using Data Mining." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ymwg3a.

Song, Zi-kong, and 宋子康. "A research on mining association rules in data stream." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/sx7u24.

Tsai, Yi-Ting, and 蔡依庭. "Application of Data Mining Techniques to Film Market Research." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/16605744340914078907.

Lin, Shih-Hau, and 林施豪. "Data Mining in Bioinformatic Contents Research - with Biological Genetic Database." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/50107356145725150045.

Lin, De-Fong, and 林德豐. "Applying Data Mining Technology in Network Behavior Anomaly Performance Research." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/22513426476489517747.

ISUN, WU YI, and 吳毅尊. "Research of Agent Technology on Data Mining of Enterprise's Knowledge." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/15491769030768636621.

Wu, Tzu-Cheng, and 吳自晟. "The Research of Data Mining Techniques applied to Insurance CRM." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/3362mt.

Chiu, Jheng-Ci, and 邱政琦. "Research on the Predictions of Fire Incidents with Data Mining." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/23dkp7.

Wu, Yen-Lin, and 吳彥霖. "A Research of Using Data Mining Techniques in Retrieving Researchers." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/why3sw.

Hung, Pin-Kai, and 洪斌凱. "Using data mining methodology to research C company accessory market." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/25149984457867750574.

SU, YUNG-HSIANG, and 蘇詠翔. "Data Mining Methods for the Research of E-stock Transaction." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/68222088478818233241.

YU, LI-HSUAN, and 余立宣. "Research on the Repairing Information Technology Devices Using Data Mining." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/76957267545832294507.

Hsieh, Ju-Cheng, and 謝儒誠. "Research of using data mining technique for automatic documents clustering." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/u47m93.

Chang, Yu-Jen, and 張于仁. "Apply Data Mining Integrated with Hierarchical Learning Architecture in the Research of Data Classification." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/34876736036020662364.

Wu, Tai-Long, and 吳台隆. "A Research of Taiwan’s Vessel Smuggling Analysis-Applied Data-Mining Technique." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/92259244989853093179.

Chang, Yung-Ku, and 張永固. "Data Mining on Target Marketing Research: An Example of Telecommunication Users." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/97687487797728422391.

Huang, Ya-Fang, and 黃雅芳. "Using Data Mining to Assess the Research of Coronary Artery Disease." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/99992822280891583455.

WANG, CHEN YUNG, and 陳永旺. "Apply data mining technology in the index fund investment strategy research." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66307548507508554222.

Liao, Ko-Hsuan, and 廖克軒. "Campus Electricity Meter Data Mining and Application of Visualisation Initial Research." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v9scpf.

Wang, Hsi-Chin, and 王璽欽. "The Research of Using Data Mining Technology in The Patent Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/dz4g92.

Madsen, Jacob Hastrup. "Outlier detection for improved clustering : empirical research for unsupervised data mining." Master's thesis, 2018. http://hdl.handle.net/10362/34464.

Bonates, Tiberius. "Optimization in logical analysis of data." 2007. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15788.

Lin, Tinghao, and 林鼎浩. "Research on Constructing a Data Mining Framework for Semiconductor Manufacturing Data and the Empirical Study." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/00819160195077260729.

Research Topics in Data Mining

Research Topics in Data Mining provide you innovative and newfangled ideas to explore your knowledge in research. We have a research team that consists of top-level experts and versatile developers to provide precise research guidance for research scholars and students. We have 5000+ happy customers from all over the world, and still, we are providing support for them through our 120+ branches situated in various countries. And also, we attained the world’s topmost position among other renowned institute in research.

We also have a tie-up with the standard universities and colleges to provide the best research guidance for the research scholars and students. And also, We have to deal with a complex problem to provide an accurate solution with the help of our experienced professionals in research. We never give up on your research concepts once we committed to you. Our work will prove who we are? What is our standard? How was the experience with us?

Topics in Data Mining

Research Topics in Data Mining offer you nurture platform to shine your research career successfully. Data mining is used to mining meaningful information from large datasets. So we provide support for knowledge mining, i.e., we mine the best and innovative ideas for your project with our top experts’ help. We also provide support for all the students like undergraduate (BE, BTech) postgraduate (ME, MTech, MCA), and research scholars (MPhil, MS, PhD).

We also support research scholars and students in various data mining domains like frequent itemset mining, web mining, opinion mining, etc. In the past 10+ years, we are working in this field, and also, many students are benefited from our smart work. Now let’s have a glance at data mining for your review,

—”Data mining is a technique which is also used to extract meaningful information from a huge database. It is used in many research areas, including mathematics, cybernetics, genetics, and also marketing. The benefit of data mining is to find uncover hidden patterns and also relationships in data that can be used to make predictions that impact businesses.”

Key Features of Data-Mining

Visualization, learning and also statistical techniques support
Focus on large data set and also databases
Automatic discovery of patterns
Python, R, Lisp/Clojure, SQL, also Matlab programming languages used

Graphical user interface support

Rattle gui:.

Free and open source package
R statistical software and also programming support
Used for statistical analysis or model generation
It allows for the dataset to be partitioned [training, validation and also testing]
Machine learning algorithms serves as collection
Open source software also based on java
Primarily designed as a tool for analyzing data [agricultural domains]
Its also graphical user interfaces allow user to easy use.

Oracle Data Miner GUI:

Oracle SQL developer extension
Automation, scheduling and also deployment using SQL and PL/SQL scripts
It enables data analysts to view their data and also accelerate model deployment
Used to built and also evaluate multiple machine learning/data mining models
Open source software (Python based)
Data analysis and also visualization purpose
Support python library also for data manipulation and widget alteration in advanced users
Used to create lining predefined or user-designed widgets

Rapid Miner:

Java based open source platform
Predictive analysis purpose
Used in client/server model with the server offered as either on-premise, or in public or private cloud infrastructures

Database Used

Oracle database 12c:.

It is also purposely designed for the cloud
Composed of oracle database 11g release 2, SQL developer, also Data Miner Repository
Faster and simpler, schema-based consolidation without changes to existing applications

SQL Server:

Provide add-in for:

Microsoft office Visio 2010 (Data mining templates)
Micro-soft office excel (Table analysis tools and also data mining clients)

Apache Mahout:

Data mining library and also scalable learning
Scalable machine learning and also data mining
Managing, writing and also learning large datasets
It provide data summarization, query and also analysis

Most Commonly Used Algorithms and Methods:

Neural Networks
Genetic algorithms
Nearest neighbor method
Semi-supervised learning
Rule reduction
Data visualization
Statistical algorithm
Regression algorithm

-Stepwise regression

-Logistic regression

-Locally estimated scatter plot smoothing

-Ordinary least squares regression

-Linear regression

-Multivariate adaptive regression splines

Supervised learning
Instance based algorithms

-K-Nearest Neighbor

-Locally weighted learning

-Learning vector quantization

-And also in Self-organizing map

RSM and CHAID
EM algorithm

Frameworks and Libraries Used

Apache Mahout
Microsoft OLE DB also for Data mining
Text Analyst COM
PolyAnalyst COM
Data Mining template library
Knowledge STUDIO SDK
NAG Data mining components
Machine learning framework
Wiz [Open java data mining and also knowledge discovery platform]
Java data mining package
Dlib C++ library
Mloss [Machine learning open source software]
YCML [Optimization and also machine learning algorithms]
Very fast machine learning library
Scikit learn
XELOPES [Also For embedded data mining]

Recent Research Applications

Market Basket Analysis
Educational data mining
Manufacturing engineering
Bio informatics
Research analysis [Data pre-processing, data cleaning and also database integration]
Criminal investigation
Customer segmentation applications
Corporate surveillance and also financial banking
Sentiment analysis and also Lie detection
Intrusion detection and also fraud detection
Customer relationship management
Education data mining
Future health care also based on applications

Approaches used:

-Machine learning

-Statistics

-Data visualization

-And also Machine learning

Recent Research Topics

Data mining techniques also for bankruptcy prediction
Data mining and forensic techniques also for an internal intrusion detection and protection system
Mining frequent itemsets on temporal data also using new methodology
Diabetes therapy management by data stream mining also using real time decision rules
Hashing and lexicographie order in hardware also for approximate frequent itemsets mining on data streams
Data mining and web technology also for department automation system
Building cooling load prediction and also for energy efficiency improvement using mining big building operational data
Business intelligence also using an advanced inventory data mining system
Data mining for effect of temperature and also rainfall of paddy yield

The above information will give you an understanding of Data-Mining to get a clear vision of data mining. We also provide additional support for Project development, Thesis writing, Journal paper writing and also Journal publication, etc. If you also have any questions or comments, please get in touch with us. Our tutors are also waiting for communication with you to provide support for your research convenience. Our online service is available 24×7.

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Home > Statler College of Engineering and Mineral Resources > MININGENG > Mining Engineering Graduate Theses and Dissertations

Mining Engineering Graduate Theses and Dissertations

Theses/dissertations from 2023 2023.

Development of A Hydrometallurgical Process for the Extraction of Cobalt, Manganese, and Nickel from Acid Mine Drainage Treatment Byproduct , Alejandro Agudelo Mira

Selective Recovery of Rare Earth Elements from Acid Mine Drainage Treatment Byproduct , Zeynep Cicek

Identification of Rockmass Deformation and Lithological Changes in Underground Mines by Using Slam-Based Lidar Technology , Francisco Eduardo Gil Hurtado

Analysis of the Brittle Failure Mechanism of Underground Stone Mine Pillars by Implementing Numerical Modeling in FLAC3D , Rosbel Jimenez

Analysis of the root causes of fatal injuries in the United States surface mines between 2008 and 2021. , Maria Fernanda Quintero

AUGMENTED REALITY AND MOBILE SYSTEMS FOR HEAVY EQUIPMENT OPERATORS IN SURFACE MINING , Juan David Valencia Quiceno

Theses/Dissertations from 2022 2022

Integrated Large Discontinuity Factor, Lamodel and Stability Mapping Approach for Stone Mine Pillar Stability , Mustafa Baris Ates

Noise Exposure Trends Among Violating Coal Mines, 2000 to 2021 , Hanna Grace Davis

Calcite depression in bastnaesite-calcite flotation system using organic acids , Emmy Muhoza

Investigation of Geomechanical Behavior of Laminated Rock Mass Through Experimental and Numerical Approach , Qingwen Shi

Static Liquefaction in Tailing Dams , Jose Raul Zela Concha

Experimental and Theoretical Investigation on the Initiation Mechanism of Low-Rank Coal's Self-Heating Process , Yinan Zhang

Development of an Entry-Scale Modeling Methodology to Provide Ground Reaction Curves for Longwall Gateroad Support Evaluation , Haochen Zhao

Size effect and anisotropy on the strength of shale under compressive stress conditions , Yun Zhao

Theses/Dissertations from 2021 2021

Evaluation of LIDAR systems for rock mass discontinuity identification in underground stone mines from 3D point cloud data , Mario Alejandro Bendezu de la Cruz

Implementing the Empirical Stone Mine Pillar Strength Equation into the Boundary Element Method Software LaModel , Samuel Escobar

Recovery of Phosphorus from Florida Phosphatic Waste Clay , Amir Eskanlou

Optimization of Operating Conditions and Design Parameters on Coal Ultra-Fine Grinding Through Kinetic Stirred Mill Tests and Numerical Modeling , Francisco Patino

The Effect of Natural Fractures on the Mechanical Behavior of Limestone Pillars: A Synthetic Rock Mass Approach Application , Mustafa Can Süner

Evaluation of Various Separation Techniques for the Removal of Actinides from A Rare Earth-Containing Solution Generated from Coarse Coal Refuse , Deniz Talan

Geology Oriented Loading Approach for Underground Coal Mines , Deniz Tuncay

Various Operational Aspects of the Extraction of Critical Minerals from Acid Mine Drainage and Its Treatment By-product , Zhongqing Xiao

Theses/Dissertations from 2020 2020

Adaptation of Coal Mine Floor Rating (CMFR) to Eastern U.S. Coal Mines , Sena Cicek

Upstream Tailings Dam - Liquefaction , Mladen Dragic

Development, Analysis and Case Studies of Impact Resistant Steel Sets for Underground Roof Fall Rehabilitation , Dakota D. Faulkner

The influence of spatial variance on rock strength and mechanism of failure , Danqing Gao

Fundamental Studies on the Recovery of Rare Earth Elements from Acid Mine Drainage , Xue Huang

Rational drilling control parameters to reduce respirable dust during roof bolting operations , Hua Jiang

Solutions to Some Mine Subsidence Research Challenges , Jian Yang

An Interactive Mobile Equipment Task-Training with Virtual Reality , Lazar Zujovic

Theses/Dissertations from 2019 2019

Fundamental Mechanism of Time Dependent Failure in Shale , Neel Gupta

A Critical Assessment on the Resources and Extraction of Rare Earth Elements from Acid Mine Drainage , Christopher R. Vass

Time-dependent deformation and associated failure of roof in underground mines , Yuting Xue

Theses/Dissertations from 2018 2018

Parametric Study of Coal Liberation Behavior Using Silica Grinding Media , Adewale Wasiu Adeniji

Three-dimensional Numerical Modeling Encompassing the Stability of a Vertical Gas Well Subjected to Longwall Mining Operation - A Case Study , Bonaventura Alves Mangu Bali

Shale Characterization and Size-effect study using Scanning Electron Microscopy and X-Ray Diffraction , Debashis Das

Behaviour Of Laminated Roof Under High Horizontal Stress , Prasoon Garg

Theses/Dissertations from 2017 2017

Optimization of Mineral Processing Circuit Design under Uncertainty , Seyed Hassan Amini

Evaluation of Ultrasonic Velocity Tests to Characterize Extraterrestrial Rock Masses , Thomas W. Edge II

A Photogrammetry Program for Physical Modeling of Subsurface Subsidence Process , Yujia Lian

An Area-Based Calculation of the Analysis of Roof Bolt Systems (ARBS) , Aanand Nandula

Developing and implementing new algorithms into the LaModel program for numerical analysis of multiple seam interactions , Mehdi Rajaeebaygi

Adapting Roof Support Methods for Anchoring Satellites on Asteroids , Grant B. Speer

Simulation of Venturi Tube Design for Column Flotation Using Computational Fluid Dynamics , Wan Wang

Theses/Dissertations from 2016 2016

Critical Analysis of Longwall Ventilation Systems and Removal of Methane , Robert B. Krog

Implementing the Local Mine Stiffness Calculation in LaModel , Kaifang Li

Development of Emission Factors (EFs) Model for Coal Train Loading Operations , Bisleshana Brahma Prakash

Nondestructive Methods to Characterize Rock Mechanical Properties at Low-Temperature: Applications for Asteroid Capture Technologies , Kara A. Savage

Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility , Marcell B. B. Silveira

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges , Christopher R. Vass

Spontaneous Combustion of South American Coal , Brunno C. C. Vieira

Calibrating LaModel for Subsidence , Jian Yang

Theses/Dissertations from 2015 2015

Coal Quality Management Model for a Dome Storage (DS-CQMM) , Manuel Alejandro Badani Prado

Design Programs for Highwall Mining Operations , Ming Fan

Development of Drilling Control Technology to Reduce Drilling Noise during Roof Bolting Operations , Mingming Li

The Online LaModel User's & Training Manual Development & Testing , Christopher R. Newman

How to mitigate coal mine bumps through understanding the violent failure of coal specimens , Gamal Rashed

Theses/Dissertations from 2014 2014

Effect of biaxial and triaxial stresses on coal mine shale rocks , Shrey Arora

Stability Analysis of Bleeder Entries in Underground Coal Mines Using the Displacement-Discontinuity and Finite-Difference Programs , Xu Tang

Experimental and Theoretical Studies of Kinetics and Quality Parameters to Determine Spontaneous Combustion Propensity of U.S. Coals , Xinyang Wang

Bubble Size Effects in Coal Flotation and Phosphate Reverse Flotation using a Pico-nano Bubble Generator , Yu Xiong

Integrating the LaModel and ARMPS Programs (ARMPS-LAM) , Peng Zhang

Theses/Dissertations from 2013 2013

Column Flotation of Subbituminous Coal Using the Blend of Trimethyl Pentanediol Derivatives and Pico-Nano Bubbles , Jinxiang Chen

Applications of Surface and Subsurface Subsidence Theories to Solve Ground Control Problems , Biao Qiu

Calibrating the LaModel Program for Shallow Cover Multiple-Seam Mines , Morgan M. Sears

The Integration of a Coal Mine Emergency Communication Network into Pre-Mine Planning and Development , Mark F. Sindelar

Factors considered for increasing longwall panel width , Jack D. Trackemas

An experimental investigation of the creep behavior of an underground coalmine roof with shale formation , Priyesh Verma

Evaluation of Rope Shovel Operators in Surface Coal Mining Using a Multi-Attribute Decision-Making Model , Ivana M. Vukotic

Theses/Dissertations from 2012 2012

Calculating the Surface Seismic Signal from a Trapped Miner , Adeniyi A. Adebisi

Comprehensive and Integrated Model for Atmospheric Status in Sealed Underground Mine Areas , Jianwei Cheng

Production and Cost Assessment of a Potential Application of Surface Miners in Coal Mining in West Virginia , Timothy A. Nolan

The Integration of Geomorphic Design into West Virginia Surface Mine Reclamation , Alison E. Sears

Truck Cycle and Delay Automated Data Collection System (TCD-ADCS) for Surface Coal Mining , Patricio G. Terrazas Prado

New Abutment Angle Concept for Underground Coal Mining , Ihsan Berk Tulu

Theses/Dissertations from 2011 2011

Experimental analysis of the post-failure behavior of coal and rock under laboratory compression tests , Dachao Neil Nie

The influence of interface friction and w/h ratio on the violence of coal specimen failure , Simon H. Prassetyo

Theses/Dissertations from 2010 2010

A risk management approach to pillar extraction in the Central Appalachian coalfields , Patrick R. Bucks

The Impacts of Longwall Mining on Groundwater Systems -- A Case of Cumberland Mine Panels B5 and B6 , Xinzhi Du

Evaluation of ultrafine spiral concentrators for coal cleaning , Meng Yang

Theses/Dissertations from 2009 2009

Development of a coal reserve GIS model and estimation of the recoverability and extraction costs , Chandrakanth Reddy Apala

Application and evaluation of spiral separators for fine coal cleaning , Zhuping Che

Weak floor stability in the Illinois Basin underground coal mines , Murali M. Gadde

Design of reinforced concrete seals for underground coal mines , Rajagopala Reddy Kallu

Employing laboratory physical modeling to study the radio imaging method (RIM) , Jun Lu

Influence of cutting sequence and time effects on cutters and roof falls in underground coal mine -- numerical approach , Anil Kumar Ray

Implementing energy release rate calculations into the LaModel program , Morgan M. Sears

Modeling PDC cutter rock interaction , Ihsan Berk Tulu

Analytical determination of strain energy for the studies of coal mine bumps , Qiang Xu

Improvement of the mine fire simulation program MFIRE , Lihong Zhou

Theses/Dissertations from 2008 2008

Program-assisted analysis of the transverse pressure capacity of block stoppings for mine ventilation control , Timothy J. Batchler

Analysis of factors affecting wireless communication systems in underground coal mines , David P. McGraw

Analysis of underground coal mine refuge shelters , Mickey D. Mitchell

Theses/Dissertations from 2007 2007

Dolomite flotation of high magnesium phosphate ores using fatty acid soap collectors , Zhengxing Gu

Evaluation of longwall face support hydraulic supply systems , Ted M. Klemetti II

Experimental studies of electromagnetic signals to enhance radio imaging method (RIM) , William D. Monaghan

Analysis of water monitoring data for longwall panels , Joseph R. Zirkle

Theses/Dissertations from 2006 2006

Measurements of the electrical properties of coal measure rocks , Nikolay D. Boykov

Geomechanical and weathering properties of weak roof shales in coal mines , Hakan Gurgenli

Assessment and evaluation of noise controls on roof bolting equipment and a method for predicting sound pressure levels in underground coal mining , Rudy J. Matetic

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16 Data Mining Projects Ideas & Topics For Beginners [2024]

Introduction

A career in Data Science necessitates hands-on experience, and what better way to obtain it than by working on real-world data mining projects? This post provides a wide range of data mining project ideas for beginners. Whether you’re looking at data mining in database management systems, data mining projects in Java, or creative data mining project ideas, this list has you covered.

Today, data mining has become strategically important to organizations across industries. It not only helps in predicting outcomes and trends but also in removing bottlenecks and improving existing processes. Data mining research topics 2020 was already in the search bar of millions of users 2 years ago . It looks like this trend is about to continue in 2024 and beyond. So, if you are a beginner, the best thing you can do is work on some real-time data mining projects.

If you are just getting started in data science, making sense of advanced data mining techniques can seem daunting. Along with the plethora of data mining research topics available online , we have compiled some useful data mining project topics to support you in your learning journey.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment if you do not work on data mining projects yourself . In this article, we will be exploring some fun and exciting data mining projects and data mining research topics which beginners can work on to put their data mining knowledge to test. In this post, you will learn about top 16 data mining projects for beginners.

In this article, you will find 42 top python project ideas for beginners to get hands-on experience on Python

But first, let’s address the more important and frequently question that must be lurking in your mind: why to build data mining projects?

But before we begin, let us look at an example to decode what data mining is all about. Suppose you have a data set containing login logs of a web application. It can include things like the username, login timestamp, activities performed, time spent on the site before logging out, etc.

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Such unstructured data in itself would not serve any purpose unless it is organized systematically and analyzed to extract relevant information for the business. By applying the different techniques of data mining, you can discover user habits, preferences, peak usage timings, etc. These insights can further increase the software system’s efficiency and boost its user-friendliness. Learn more about data mining with our data science programs.

In today’s digital era, the computing processes of collecting, cleaning, analyzing, and interpreting data make up an integral part of business strategies. So, data scientists are required to have adequate knowledge of methods like pattern tracking, classification, cluster analysis, prediction, neural networks, etc. The more you experiment with different data mining projects, the more knowledge you gain.

Data Mining Project Ideas & Topics for Beginners

This list of data mining projects for students is suited for beginners, and those just starting out with Data Science in general. These data mining projects will get you going with all the practicalities you need to succeed in your career.

Further, if you’re looking for data mining project for final year, this list should get you going as this list also contains data mining projects for students . So, without further ado, let’s jump straight into some data mining projects that will strengthen your base and allow you to climb up the ladder.

1. iBCM: interesting Behavioral Constraint Miner

One of the best ideas to start experimenting you hands-on data mining projects for students is working on iBCM. A sequence classification problem deals with the prediction of sequential patterns in data sets. It discovers the underlying order in the database based on specific labels. In doing so, it applies the simple mathematical tool of partial orders. However, you would require a better representation to achieve more accurate, concise, and scalable classification. And a sequence classification technique with a behavioral constraint template can address this need.

With the iBCM project, you can delve into the field of sequence categorization. Using behavioral constraint templates, this venture predicts sequential patterns inside datasets. This method employs mathematical tools such as partial orders to reveal underlying data patterns in an accurate and simple manner. Beyond traditional sequence mining, iBCM finds a wide range of patterns, making it a good starting point for inexperienced data miners.

The interesting Behavioral Constraint Miner (iBCM) project can express a variety of patterns over a sequence, such as simple occurrence, looping, and position-based behavior. It can also mine negative information, i.e., the absence of a particular behavior. So, the iBCM approach goes much beyond the typical sequence mining representations and is a perfect starting point for those looking for data mining projects for students.

2. GERF: Group Event Recommendation Framework

This is one of the simple data mining projects yet an exciting one. It is an intelligent solution for recommending social events, such as exhibitions, book launches, concerts, etc. A majority of the research focuses on suggesting upcoming attractions to individuals. So, a Group Event Recommendation Framework (GERF) was developed to propose events to a group of users.

GERF addresses group social event recommendations by utilizing learning-to-rank algorithms for reliable choices. This project provides efficient event recommendations for a varied user population by extracting group preferences and environmental impacts, with applications ranging from exhibitions to travel services.

This model uses a learning-to-rank algorithm to extract group preferences and can incorporate additional contextual influences with ease, accuracy, and time-efficiency.

Learning to rank, also known as machine-learned ranking (MLR), is the process of building ranking models for systems needing information retrieval using machine learning techniques such as supervised learning, semi-supervised learning, and reinforcement learning.

The objects used for training are organized into lists, with the relative order between the lists being partially described. In most cases, a number or ordinal score is assigned to each item, or a binary judgment (such as “relevant” for true values(binary 1) or “not relevant” for false values(binary 0)) is made.

The objective of the ranking model is to apply the same logic used to rank the training data to the rating of fresh, unknown lists.

Also, it can be conveniently applied to other group recommendation scenarios like location-based travel services.

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3. Efficient similarity search for dynamic data streams

Online applications use similarity search systems for tasks like pattern recognition, recommendations, plagiarism detection, etc. Typically, the algorithm answers nearest-neighbor queries with the Location-Sensitive Hashing or LSH approach, a min-hashing related method. It can be implemented in several computational models with large data sets, including MapReduce architecture and streaming. Mentioning data mining projects can help your resume look much more interesting than others.

For a variety of functions, online apps rely on similarity search engines. This research focuses on effective similarity search strategies for dynamic data streams, with a special emphasis on scalability in huge datasets. Its novel features, such as the use of the Jaccard index as a similarity measure and estimating techniques based on sketching, improve accuracy in pattern recognition and recommendation tasks.

Dynamic data streams, however, require scalable LSH-based filtering and design. To this end, the efficient similarity search project outperforms previous algorithms. Here are some of its main features:

Relies on the Jaccard index as a similarity measure
Suggests a nearest-neighbor data structure feasible for dynamic data streams
Proposes a sketching algorithm for similarity estimation

4. Frequent pattern mining on uncertain graphs

Application domains like bioinformatics, social networks, and privacy enforcement often encounter uncertainty due to the presence of interrelated, real-life data archives. This uncertainty permeates the graph data as well.

Frequent pattern mining on uncertain graphs is critical in settings requiring uncertain data, such as bioinformatics and social networks. This project addresses the issue of transitive interactions with uncertain graph data. It efficiently manages real-world data archives with increased performance by utilizing enumeration-evaluation methods and approximation techniques.

This problem calls for innovative data mining projects that can catch the transitive interactions between graph nodes. This beginner-level data mining projects will help build a strong foundation for fundamental programming concepts. One such technique is the frequent subgraph and pattern mining on a single uncertain graph. The solution is presented in the following format:

An enumeration-evaluation algorithm to support computation under probabilistic semantics
An approximation algorithm to enable efficient problem-solving
Computation sharing techniques to drive mining performance
Integration of check-point based and pruning approaches to extend the algorithm to expected semantics

5. Cleaning data with forbidden itemsets or FBIs

Data cleaning methods typically involve taking away data errors and systematically fixing the issue by specifying constraints (illegal values, domain restrictions, logical rules, etc.)

Data cleansing frequently entails defining limitations to correct inaccuracies. The FBI’s effort introduces a fixing method based on banned itemset, finding constraints in dirty data automatically and improving error detection precision. Empirical evaluations establish the mechanism’s trustworthiness and dependability, which is critical in the big data scenario.

In the real-life big data universe, we are inundated with dirty data that comes without any known constraints. In such a scenario, the algorithm automatically discovers constraints on the dirty data and further uses them to identify and repair errors. But when this discovery algorithm runs on the repaired data again, it introduces new constraint violations, rendering the data erroneous. This is one of the excellent data mining projects for beginners.

Hence, a repairing method based on forbidden itemsets (FBIs) was devised to record unlikely co-occurrences of values and detect errors with more precision. And empirical evaluations establish the credibility and reliability of this mechanism.

6. Protecting user data in profile-matching social networks

This is one of the convenient data mining projects that has a lot of use in the future. Consider the user profile database maintained by the providers of social networking services, such as online dating sites. The querying users specify certain criteria based on which their profiles are matched with that of other users. This process has to be secure enough to protect against any kind of data breaches. There are some solutions in the market today that use homomorphic encryption and multiple servers for matching user profiles to preserve user privacy.

Read our popular Data Science Articles

7. privrank for social media.

Social media sites mine their users’ preferences from their online activities to offer personalized recommendations. However, user activity data contains information which can be used to infer private details about an individual (for example, gender, age, etc.) And any leak or release of such user-specified data can increase the risk of interference attacks.

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8. Practical PEKs scheme over encrypted email in cloud server

In the light of current high-profile public events related to email leaks, the security of such sensitive messages has emerged as a primary concern for users worldwide. To that end, the Public Encryption with Keyword Search (PEKS) technology offers a viable solution. This is one of the useful data mining projects in which this combines security protection with efficient search operability functions.

When searching over a sizable encrypted email database in a cloud server, we would want the email receivers to perform quick multi-keyword and boolean searches without revealing additional information to the server.

Read: Data Mining Real World Applications

9. Sentimental analysis and opinion mining for mobile networks

This project concerns post-publishing applications where a registered user can share text posts or images and also leave comments on posts. Under the prevailing system, users have to go through all the comments manually to filter out verified comments, positive comments, negative remarks, and so on.

With the sentiment analysis and opinion mining system, users can check the status of their post without dedicating much time and effort. It provides an opinion on the comments made on a post and also gives the option to view a graph.

10. Mining the k most frequent negative patterns via learning

In behavior informatics, the negative sequential patterns (NSPs) can be more revealing than the positive sequential patterns (PSPs) . For instance, in a disease or illness-related study, data on missing a medical treatment can be more useful than data on attending a medical procedure. But to the present day, NSP mining is still at a nascent stage. And the ‘Topk-NSP+’ algorithm presents a reliable solution for overcoming the obstacles in the current mining landscape. This is one of the trending data mining and this is how the project proposes the algorithm:

Mining the top-k PSPs with the existing method
Mining the to-k NSPs from these PSPs by using an idea similar to the top-k PSPs mining
Employing three optimization strategies to select useful NSPs and reduce computational costs

Also try: Machine Learning Project Ideas for Beginners

11. Automated personality classification project

The automatic system analyzes the characteristics and behaviors of participants. And after observing the past patterns of data classification, it predicts a personality type and stores its own patterns in a dataset. This project idea can be summarized as follows:

Store personality-related data in a database
Collect associated characteristics for each user
Extract relevant features from the text entered by the participant
Examine and display the personality traits
Interlink personality and user behavior (There can be varying degrees of behavior for a particular personality type)

Such models are commonplace in career guidance services where a student’s personality is matched with suitable career paths. This can be an interesting and useful data mining projects.

12. Social-Aware social influence modeling

This is one of the most popular data mining mini projects. This project deals with big social data and leverages deep learning for sequential modeling of user interests. The stepwise process is described below:

A preliminary analysis of two real datasets (Yelp and Epinions)
Discovery of statistically sequential actions of users and their social circles, including temporal autocorrelation and social influence on decision-making
Presentation of a novel deep learning model called Social-Aware Long Short-Term Memory (SA-LSTM), which can predict the type of items or Points of Interest that a particular user will buy or visit next. Long short-term memory, often known as LSTM, is a kind of neural network that is used in the domains of deep learning and artificial intelligence. LSTM neural networks have feedback connections, in contrast to more traditional feedforward neural networks so that they can change the training parameters or hyperparameters to be more precise, with each epoch. LSTM is a kind of recurrent neural network, commonly known as an RNN, which is capable of processing, not just individual data points but also complete data sequences.

Experimental results reveal that the structure of this proposed solution enables higher prediction accuracy as compared to other baseline methods.

This is one of the data mining mini projects that will definitely help you get some real-world exposure.

13. Predicting consumption patterns with a mixture approach

Individuals consume a large selection of items in the digital world today. For example, while making purchases online, listening to music, using online navigation, or exploring virtual environments. Applications in these contexts employ predictive modeling techniques to recommend new items to users. However, in many situations, we want to know the additional details of previously-consumed items and past user behavior. And this is where the baseline approach of matrix factorization-based prediction falls short. This is one of the creative data mining projects.

A mixture model with repeated and novel events offers a suitable alternative for such problems. It aims to deliver accurate consumption predictions by balancing individual preferences in terms of exploration and exploitation. Also, it is one of those data mining project topics that include an experimental analysis using real-world datasets. The study’s results show that the new approach works efficiently across different settings, from social media and music listening to location-based data.

14. GMC: Graph-based Multi-view Clustering

The existing clustering methods for multi-view data require an extra step to produce the final cluster as they do not pay much attention to the weights of different views. Moreover, they function on fixed graph similarity matrices of all views. And this is the perfect idea for your next data mining project as this can also be considered as a graph mining projects .

A novel Graph-based Multi-view Clustering (GMC) can tackle this issue and deliver better results than the previous alternatives. It is a fusion technique that weights data graph matrices for all views and derives a unified matrix, directly generating the final clusters. Other features of the graph mining projects include:

Partition of data points into the desired number of clusters without using a tuning parameter. For this, a rank constraint is imposed on the Laplacian matrix of the unified matrix.
Optimization of the objective function with an iterative optimization algorithm

15. ITS: Intelligent Transportation System

A multi-purpose traffic solution generally aims to ensure the following aspects:

Transport service’s efficiency
Transport safety
Reduction in traffic congestion
Forecast of potential passengers
Adequate allocation of resources

Consider a project that uses the above system to optimize the process of bus scheduling in a city. ITS is one of the interesting data mining projects for beginners. You can take the past three years’ data from a renowned bus service company, and apply uni-variate multi-linear regression to conduct passengers’ forecasts.

Further, you can calculate the minimum number of buses required for optimization in a Generic Algorithm. Finally, you validate your results using statistical techniques like mean absolute percentage error (MAPE) and mean absolute deviation (MAD). Mean Absolute Percentage Error(MAPE): The accuracy of a forecasting system may be quantified by calculating the mean absolute percentage error (MAPE). Measured as a percentage, it is derived by taking the sum of the absolute values of the errors across all time periods and dividing by the real values to provide a reading on how close the estimate is to the true value.

The most popular way to quantify forecast errors is via the use of the mean absolute percentage error (MAPE), perhaps because the variable’s units are already in percentage form. A lack of extremes in the data is necessary for optimal performance (and no zeros). In regression analysis and model assessment, it is frequently used as a loss function.

Mean Absolute Deviation(MAD): It measures how far each data point is from the dataset’s mean value. It helps us get a sense of the data’s overall dispersion. To find out the MAD for a data set, we must first calculate the mean and then the distance of each data point from the mean using MPD(Mean positive distances) which would yield the absolute deviation.

This absolute deviation is the measure of this gap between the mean and each data point. Now, we take the total of all these deviations, add it and then divide it by the total number of data points in the data set.

16. TourSense for city tourism

City-scale transport data about buses, subways, etc. could also be used for tourist identification and preference analytics. But relying on traditional data sources, such as surveys and social media, can result in inadequate coverage and information delay.

The TourSense project demonstrates how to override such shortcomings and provide more valuable insights. This tool would be useful for a wide range of stakeholders, from transport operators and tour agencies to tourists themselves. This is one of the excellent data mining projects for beginners. Here are the main steps involved in its design:

A graph-based iterative propagation learning algorithm to identify tourists from other public commuters
A tourist preference analytics model (utilizing the tourists’ trace data) to learn and predict their next tour
An interactive UI to serve easy information access from the analytics

Data Mining Projects: Conclusion

In this article, we have covered 16 data mining projects. If you wish to improve your data mining skills, you need to get your hands on these data mining projects.

Dive into Data Science involves more than just academic understanding; it also necessitates practical experience. These data mining project ideas are designed for novices, with options to investigate sequence classification, group suggestions, similarity search, graph mining, and data cleaning. As you work on these projects, you’ll lay a solid foundation in Data Science and prepare for future challenges in this ever-changing area.

Data mining and correlated fields have experienced a surge in hiring demand in the last few years as data mining research topics 2020 was already in the search bar of millions of users 2 years ago and is still there . With the above data mining project topics, you can keep up with the market trends and developments. So, stay curious and keep updating your knowledge!

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Rohit Sharma

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Frequently Asked Questions (FAQs)

As the name suggests, data mining refers to the process of mining or extraction of patterns from large data sets. The methods it involves include the combined knowledge of machine learning, statistics, and database systems. Before applying data mining techniques, you need to assemble a large dataset that must be large enough to contain patterns to be mined. There are 6 prominent steps that are involved in the data mining process. These steps are anomaly detection, association rule learning, clustering, classification, regression, and summarization.

Classification in data mining allows enterprises to arrange large sets of data according to the target categories. Once ordered in this manner, the enterprises could see the data clearly and analyze the risks and profits easily which in turn helps the businesses to grow. Classification can also be understood as a way to generalize known structures to apply to new data. The analysis is based on several patterns that are found in the data. These patterns help to sort the data into different groups.

Projects are all about experimenting and testing your skills. They let you use all of your creativity and develop a useful product out of it. Building data mining projects will not only give you hands-on experience but will also enhance your knowledge pool. You can add these amazing projects to your resume to showcase your skills to potential employers. These projects will help you to implement your theoretical knowledge into action and gain practical benefits from it.

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Top 4 Trending Thesis Topics in Data Mining [Capstone Project Ideas]
140 Excellent Big Data Research Topics to Consider
Professional Research Guidance
PhD Thesis Topics in Data Mining (Thesis Writing Help)
Top 5 Data Mining Dissertation Topics
Trending Top 10 Data Mining Thesis Topics [How to Choose Novel Idea]

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82 Data Mining Essay Topic Ideas & Examples
Commercial Uses of Data Mining. Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations. A Discussion on the Acceptability of Data Mining.
105 Data Mining Essay Topic Ideas & Examples
Data mining is a powerful tool that helps businesses and organizations uncover hidden patterns, trends, and insights from large datasets. It involves the process of extracting valuable information from raw data, which can then be used for various purposes such as improving decision-making, predicting future outcomes, and understanding customer behavior.
Trending Data Mining Thesis Topics
Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining. These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis.
data mining Latest Research Papers
The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm. Download Full-text.
(PDF) Trends in data mining research: A two-decade review using topic
Abstract and Figures. This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied ...
Latest Research and Thesis topics in Data Mining
Topics to study in data mining. Data mining is a relatively new thing and many are not aware of this technology. This can also be a good topic for M.Tech thesis and for presentations. Following are the topics under data mining to study: Fraud Detection. Crime Rate Prediction.
Data Mining Dissertation Topics
Data Mining Dissertation Topics. The term "data mining" refers to an intelligent data lookup capacity that uses statistics-based algorithms and methodologies to find trends, patterns, links, and correlations within the collected data and records. Audio, Pictorial, Video, textual, online, and social media-based mining are only a few examples ...
80 Data Mining Research Topics
These dissertation topics in Data Mining provide ample opportunities to research for undergraduate, master's, and doctoral students. 44-20-8133-2020. ... In this comprehensive guide, we will delve into an array of intriguing data mining research topics, spanning various complexity levels and domains, to help you navigate the fascinating ...
Data Mining
An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks Author: Remmits, Y. L. J. A., 30 Sept 2019 Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach) Student thesis: Master
Innovative Data Mining Research Topics (Research Guidance)
Data Mining Research Topics is our research package where we offer thousands of research topics for students and research scholars. Scholars always seek perfect guidance also for their project completion. They want to make sure that they came in safe hands when it comes to framing their thesis. We tell you that you can also cease your search ...
Open Theses
Open Topics We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A non-exhaustive list of open topics is listed below.. If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential ...
Dissertations / Theses on the topic 'Data mining'
Consult the top 50 dissertations / theses for your research on the topic 'Data mining.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
100 Best Big Data Research Topics
These are great data research topics that learners at different study levels should consider when asked to write academic papers or essays. However, extensive research is required to come up with great write-ups on these topics. Data Mining Research Topics for Students. Data mining refers to the extraction of useful information from raw data.
data mining
First, talk to your thesis advisor before committing to a project. They know better than I do. Secondly, just analyzing a new dataset using standard techniques doesn't make for a good masters thesis. Your project is expected to use some sort of novel approach.
Dissertations / Theses: 'Data mining
Consult the top 50 dissertations / theses for your research on the topic 'Data mining - Research.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago ...
Innovative Research Topics on Data Mining (Latest Titles)
Research Topics on Data Mining offer you creative ideas to prime your future brightly in research. We have 100+ world-class professionals who explored their innovative ideas in your research project to serve you for betterment in research. So We have conducted 500+ workshops throughout the world, and a large number of researchers and students ...
PDF Data Mining Thesis Topics in Finland
Different data mining techniques were applied to the Theseus dataset to build a web application to explore thesis topics and degree programmes using different libraries in Python and JavaScript. Thesis topics were extracted from manually annotated keywords by the authors and curated subjects by the librarians.
PDF The application of data mining methods
This thesis first introduces the basic concepts of data mining, such as the definition of data mining, its basic function, common methods and basic process, and two common data mining methods, classification and clustering. Then a data mining application in network is discussed in detail, followed by a brief introduction on data mining ...
MASTER'S THESIS
objective in this thesis is to get a deeper knowledge of the data mining area and evalu-ate methods to automatically classify the data in structured personal registries. In this report I'll describe di erent methods and algorithms used in data mining, apply a few selected popular algorithms on two data sets and then discuss the result. i
Trending Research Topics in Data Mining (PhD Guidance)
Research Topics in Data Mining offer you nurture platform to shine your research career successfully. Data mining is used to mining meaningful information from large datasets. So we provide support for knowledge mining, i.e., we mine the best and innovative ideas for your project with our top experts' help. We also provide support for all the ...
PDF Data Mining in social media: An Analysis of Techniques and Applications
Techniques for data mining in social media Data mining techniques are used in social media to identify trends and patterns in the content that users create. These techniques are very useful for analyzing the data that organizations collect from these platforms. This section discussed about some of the most common data mining techniques used in ...
Mining Engineering Graduate Theses and Dissertations
Truck Cycle and Delay Automated Data Collection System (TCD-ADCS) for Surface Coal Mining, Patricio G. Terrazas Prado. PDF. New Abutment Angle Concept for Underground Coal Mining, Ihsan Berk Tulu. Theses/Dissertations from 2011 PDF. Experimental analysis of the post-failure behavior of coal and rock under laboratory compression tests, Dachao ...
16 Data Mining Projects Ideas & Topics For Beginners [2024]
2. GERF: Group Event Recommendation Framework. This is one of the simple data mining projects yet an exciting one. It is an intelligent solution for recommending social events, such as exhibitions, book launches, concerts, etc. A majority of the research focuses on suggesting upcoming attractions to individuals.

82 Data Mining Essay Topic Ideas & Examples

105 Data Mining Essay Topic Ideas & Examples

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