data mining thesis topics

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Master thesis topics [closed]

I am looking for a thesis to complete my master, I am interested in Predictive Analytics in marketing, HR, management or financial subject, using Data Mining Application.

I have found a very interesting subject: "Predicting customer churn using decision tree" or either "Predicting employee turnover using decision tree", I looked around very hard but unfortunately couldn't find any relevant dataset to download ( Telecommunication Customer churn Dataset ).

I would like to work on a similar subject using "Decision Tree Technique".

Please suggest some topics or project that would make for a good masters thesis subject.

data-mining
predictive-modeling
decision-trees

2 Answers 2

This is the approach I took:

Find journals related to your field of studies
Skim through the proceedings, see if there are titles that catch your interest
Read the papers (carefully or globally) that seemed interesting
Carefully consider the approaches and whatever future suggestions they present in their papers
Think critically: What would you change? What do you want to find out? Don't limit yourself to data but rather orient from the perspective of research. Solutions for data might only become apparent when you know exactly what you want to examine.

I think this has advantages because these papers outline details regarding data as well -- perhaps you can use the same.

Present some papers and your idea to your prospective supervisor and he/she will make some suggestions. Researchers generally have a lot of knowledge about the possibilities and might even be curious about some things themselves.

Good luck! And enjoy.

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.

With that said, I'd suggest that you start by reading up on existing decision tree techniques, learning why they work and what their flaws are, and try to find ways to overcome the flaws. Then, once you have your improvement, it should be relatively easy to find a dataset to apply it to.

Not the answer you're looking for? Browse other questions tagged data-mining predictive-modeling bigdata decision-trees research or ask your own question .

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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

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

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

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

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

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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16 Data Mining Projects Ideas & Topics For Beginners [2023]

Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Table of Contents

Data Mining Projects

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 2023 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.

Our learners also read : Python online course free !

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.

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.

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.

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:

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.

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:

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.)

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.

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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:

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:

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:

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:

15. ITS: Intelligent Transportation System

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

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.

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:

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.

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.

What do you mean by data mining?

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.

Discuss the significance of classification in data mining.

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.

Why should I build projects in data mining?

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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Thesis Topics in Data Mining - Blog.DoMyPapers.com
Data mining refers to the process of discovering patterns and knowledge from massive amounts of data. The process lies in employing different prospectives to data analysis and generating useful information. It has been applied to a variety of domains, such as Web research, scientific discovery, business intelligence, and homeland security.
Trending Data Mining Thesis Topics - PhD Direction
How does data mining work? Senior Research Member. Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and ... Research Experience. Journal Member. Book Publisher. Research Ethics.
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Thesis and Research Topics in Data Mining. Web Mining. Web mining is an application of data mining for discovering data patterns from the web. Web mining is of three categories – content ... Predictive Analytics. Oracle Data Mining. Clustering. Text mining.
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Most Interesting Data Mining Topics to Write about We will write a custom essay specifically for you! Get your first paper with 15% OFF Learn More 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
data mining - Master thesis topics - Data Science Stack Exchange
I am looking for a thesis to complete my master, I am interested in Predictive Analytics in marketing, HR, management or financial subject, using Data Mining Application. I have found a very interesting subject: "Predicting customer churn using decision tree" or either "Predicting employee turnover using decision tree", I looked around very hard but unfortunately couldn't find any relevant dataset to download ( Telecommunication Customer churn Dataset ).
Top 5 Data Mining Dissertation Topics - Research Issues ...
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.
16 Data Mining Projects Ideas & Topics For Beginners [2023]
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: