IEEE Data Mining Projects 2026 — Final Year Data Mining Projects for BE, BTech & MTech CSE/IT Students in Bangalore
Data mining final year projects are among the most fundamental and widely accepted IEEE project categories for BE, BTech and MTech CSE and IT students. Data mining is the process of extracting previously unknown, valid and actionable patterns and knowledge from large datasets — using algorithms such as Apriori, FP-Growth, K-Means, DBSCAN, C4.5 decision tree, Naive Bayes, SVM, Random Forest and XGBoost. At ProjectsatBangalore, we deliver 12+ IEEE 2026 data mining project topics across 12 specialised mining domains, each with Python or Weka source code, preprocessed dataset, algorithm output screenshots, university-format project report and PPT.
Every IEEE data mining project from our Bangalore centre comes with the IEEE Xplore 2026 base paper, complete source code in Python (scikit-learn, mlxtend, NLTK, NetworkX) or Weka (.arff files and Weka model output), dataset links, architecture and algorithm flow diagram, and a comprehensive viva Q&A document covering data mining fundamentals, algorithm complexity, preprocessing techniques, evaluation metrics and real-world application discussion.
Data Mining Project Categories Available
- Association rule mining projects using Apriori and FP-Growth
- Classification data mining projects using Decision Tree, SVM, Random Forest
- Clustering data mining projects using K-Means, DBSCAN, Hierarchical
- Web mining projects for usage, content and structure analysis
- Text mining and opinion mining projects using NLP
- Healthcare data mining projects for disease prediction and diagnosis
- Educational data mining projects for student performance prediction
- Social network mining and community detection projects
- Anomaly detection data mining projects for fraud and intrusion
- Sequential pattern mining projects for clickstream and basket data
- Privacy-preserving data mining projects (k-anonymity, l-diversity)
- Distributed data mining projects using Hadoop MapReduce and Spark
Data Mining Tools, Algorithms & Platforms
Industry and academic tools used across all 12 data mining project domains — from classical Apriori rule mining and Weka-based classification to deep NLP text mining and graph-based social network analysis.
Python 3.11
scikit-learn
Weka 3.9
mlxtend (Apriori)
Pandas / NumPy
XGBoost / LightGBM
NLTK / SpaCy
NetworkX (Graphs)
R / arules pkg
Matplotlib / Seaborn
Orange Data Mining
Apache Mahout
Jupyter / Google Colab
Hugging Face (Text)
Core Data Mining Algorithms Covered in Final Year Projects
Apriori & FP-Growth
Frequent itemset mining, association rules, support-confidence-lift analysis
C4.5 / J48 Decision Tree
Information gain-based classification, pruning, interpretable rule sets
K-Means & DBSCAN
Centroid-based and density-based clustering for customer and spatial segmentation
Naive Bayes & KNN
Probabilistic and instance-based classification for medical and text mining
SVM & Kernel Methods
Hyperplane classification, RBF kernel, one-class SVM for anomaly mining
Random Forest & XGBoost
Ensemble methods for high-accuracy medical, financial and educational mining
Graph-Based Mining
PageRank, community detection, Girvan-Newman algorithm for social networks
Web Content & Usage Mining
TF-IDF, hyperlink analysis, clickstream sequential mining for web personalisation
PrefixSpan & SPADE
Sequential pattern mining for user session analysis and purchase sequence discovery
k-Anonymity & l-Diversity
Data perturbation and generalisation techniques for privacy-preserving mining
EDM — Bayesian & Regression
Educational data mining methods for student dropout and performance prediction
Distributed Mining (Hadoop)
MapReduce-based parallel data mining for large-scale datasets
12+ IEEE 2026 Data Mining Final Year Project Topics
Browse all IEEE data mining project ideas by domain — each topic includes IEEE 2026 base paper, Python / Weka source code, preprocessed dataset, evaluation metrics, architecture diagram, university-format project report and viva support for BE, BTech and MTech CSE/IT students.
Association Rule Mining Projects
Apriori Algorithm · FP-Growth · Market Basket Analysis · Frequent Pattern Mining · Support-Confidence-Lift · Cross-Sell Analytics
| # | IEEE 2026 Data Mining Project Title — Association Rule Mining | Domain | Tools Used |
|---|---|---|---|
| 01 | Retail Market Basket Analysis Using Apriori and FP-Growth Algorithms for Cross-Sell Recommendation — mines frequent itemsets from supermarket transactional data using both Apriori and FP-Growth, compares their computational performance, generates high-confidence association rules and visualises a product co-purchase network to drive cross-selling strategy. A classic data mining final year project with strong IEEE literature backing and real-world business impact. Best suited for data mining mini projects or standard BE final year projects. | Association Mining | Python · mlxtend · Pandas · Matplotlib · NetworkX |
| 02 | Healthcare Drug Co-Prescription Association Mining Using FP-Growth on Electronic Health Records — applies FP-Growth frequent pattern mining on anonymised EHR prescription datasets to identify drug co-prescription patterns, flag potential adverse drug combinations and generate clinical association rules with statistical confidence and lift measures. An MTech-level IEEE data mining project ideal for healthcare domain CSE students, published at IEEE Xplore 2025. | Association MiningHealthcare | Python · mlxtend · Pandas · Weka · MIMIC-III dataset |
Classification Data Mining Projects
Decision Tree · Random Forest · SVM · Naive Bayes · KNN · XGBoost · Comparative Classifier Analysis · Weka Classification
| # | IEEE 2026 Data Mining Project Title — Classification | Domain | Tools Used |
|---|---|---|---|
| 03 | Comparative Study of Data Mining Classification Algorithms for Diabetes Prediction Using PIMA Indian Dataset — implements and compares six classification algorithms — Decision Tree (J48), Naive Bayes, KNN, SVM, Random Forest and XGBoost — on the PIMA Indian Diabetes dataset, evaluating accuracy, precision, recall, F1-score and ROC-AUC to identify the best-performing classifier. A well-structured data mining major project or MTech data mining project with clear evaluation framework and IEEE 2026 base paper. | ClassificationHealthcare | Python · scikit-learn · XGBoost · Weka · PIMA Dataset |
| 04 | Intrusion Detection System Using Ensemble Data Mining Classification on NSL-KDD Network Traffic Dataset — builds a network intrusion detection system by applying multiple classification data mining algorithms (Random Forest, Gradient Boosting, SVM) on the NSL-KDD dataset, using feature selection with information gain and evaluating detection rate, false alarm rate and overall accuracy. A strong IEEE data mining project for cybersecurity-interested MTech CSE students. | Classification | Python · scikit-learn · XGBoost · Weka · NSL-KDD |
Clustering Data Mining Projects
K-Means · DBSCAN · Hierarchical Clustering · BIRCH · Customer Segmentation · Geospatial Clustering · Silhouette Analysis
| # | IEEE 2026 Data Mining Project Title — Clustering | Domain | Tools Used |
|---|---|---|---|
| 05 | Customer Segmentation Using K-Means and DBSCAN Clustering Data Mining for Targeted Marketing Analytics — applies RFM feature engineering on an e-commerce transaction dataset, then compares K-Means and DBSCAN clustering algorithms using silhouette score, Davies-Bouldin index and cluster visualisation with PCA 2D projection to segment customers into high-value, at-risk and dormant groups. An excellent data mining final year project combining clustering and business analytics for BE and MTech CSE students. | Clustering | Python · scikit-learn · Pandas · Matplotlib · Seaborn |
| 06 | Crime Pattern Analysis and Hotspot Detection Using DBSCAN Geospatial Clustering Data Mining — uses DBSCAN density-based clustering on city crime incident datasets to discover geospatial crime clusters, identify high-density crime hotspots and temporal patterns by hour and day, with an interactive Folium choropleth map for law enforcement decision support. A socially relevant MTech data mining project with strong IEEE publication alignment. | Clustering | Python · scikit-learn · Folium · GeoPandas · Matplotlib |
Web Mining Projects
Web Content Mining · Web Usage Mining · Web Structure Mining · Clickstream Analysis · PageRank · Web Scraping · Personalisation
| # | IEEE 2026 Data Mining Project Title — Web Mining | Domain | Tools Used |
|---|---|---|---|
| 07 | Web Usage Mining for Personalised Content Recommendation Using Sequential Pattern Mining on Clickstream Logs — pre-processes Apache web server access logs into user session sequences, applies PrefixSpan sequential pattern mining to discover common navigation paths and builds a collaborative-filtering-style content recommender based on discovered usage patterns. A practical web mining project and strong IEEE data mining project for e-commerce and news portal contexts, with IEEE 2026 base paper and complete Python implementation. | Web Mining | Python · PySpark / mlxtend · Pandas · Flask · MySQL |
Text Mining & Opinion Mining Projects
TF-IDF · LDA Topic Modelling · Sentiment Mining · Opinion Mining · Document Clustering · NER · Review Mining
| # | IEEE 2026 Data Mining Project Title — Text & Opinion Mining | Domain | Tools Used |
|---|---|---|---|
| 08 | Aspect-Level Opinion Mining from Amazon Product Reviews Using LDA Topic Modelling and VADER Sentiment Analysis — mines Amazon review corpus using LDA (Latent Dirichlet Allocation) to extract product aspect topics (battery, screen, price, performance), then scores aspect-level sentiment using VADER and BERT-based fine-tuning, generating a structured aspect-sentiment matrix visualised with heatmaps. A highly relevant text mining project and opinion mining project with strong IEEE 2026 journal alignment and real-world NLP application. | Text Mining | Python · NLTK · Gensim LDA · VADER · scikit-learn · Matplotlib |
Healthcare Data Mining Projects
Disease Prediction · Clinical Diagnosis Mining · Cancer Classification · EHR Mining · Readmission Mining · Survival Analysis
| # | IEEE 2026 Data Mining Project Title — Healthcare Data Mining | Domain | Tools Used |
|---|---|---|---|
| 09 | Heart Disease Risk Prediction Using Hybrid Data Mining: Feature Selection, SMOTE Oversampling and Ensemble Classification — addresses class imbalance in UCI Cleveland Heart Disease dataset using SMOTE, applies chi-square and Recursive Feature Elimination (RFE) for feature selection, trains and compares Random Forest, Gradient Boosting, SVM and Naive Bayes classifiers, and evaluates with ROC-AUC, specificity and Matthews Correlation Coefficient. A well-rounded healthcare data mining project accepted at leading IEEE journals with Weka and Python dual implementation for BE and MTech students. | Healthcare Mining | Python · scikit-learn · Weka · imbalanced-learn · XGBoost |
Educational Data Mining Projects
Student Performance Prediction · Dropout Detection · LMS Log Mining · Course Recommendation · Engagement Mining · EDM Clustering
| # | IEEE 2026 Data Mining Project Title — Educational Data Mining | Domain | Tools Used |
|---|---|---|---|
| 10 | Student Academic Performance Prediction and At-Risk Early-Warning System Using Educational Data Mining on LMS Logs — extracts behavioural features from Open University LMS dataset (OULAD) — login frequency, resource views, assignment submission timing, forum engagement — applies classification data mining (Random Forest, Decision Tree, XGBoost) to predict final grade and identify at-risk students for early intervention. A complete, socially impactful educational data mining project with IEEE 2026 base paper, Python source code and university-format report suitable for CSE and IT BE and MTech final year. | EDM | Python · scikit-learn · XGBoost · Pandas · OULAD Dataset |
| # | IEEE 2026 Data Mining Project Title — Social Network Mining | Domain | Tools Used |
|---|---|---|---|
| 11 | Community Detection and Influencer Identification in Social Networks Using Graph-Based Data Mining (Girvan-Newman & Louvain) — constructs a real-world social graph from Twitter follower / Facebook friendship data, applies the Girvan-Newman betweenness-based community detection and Louvain modularity optimisation algorithms, identifies community clusters and ranks top influencers by PageRank and authority score. A visually compelling social network mining project with interactive graph visualisation using Gephi, and a strong IEEE data mining project for MTech research scholars interested in network science and social media analytics. | Social Network Mining | Python · NetworkX · Gephi · Louvain Community · Matplotlib |
Anomaly Detection Data Mining Projects
Isolation Forest · One-Class SVM · LOF · Autoencoder Anomaly · Credit Card Fraud · Network Intrusion · Statistical Outlier Mining
| # | IEEE 2026 Data Mining Project Title — Anomaly Detection | Domain | Tools Used |
|---|---|---|---|
| 12 | Credit Card Transaction Fraud Detection Using Anomaly Detection Data Mining — Isolation Forest, LOF and One-Class SVM Comparison — addresses extreme class imbalance in the Kaggle Credit Card Fraud dataset (0.172% fraud) by comparing three unsupervised anomaly detection data mining approaches — Isolation Forest, Local Outlier Factor (LOF) and One-Class SVM — using precision-recall curve and area under precision-recall curve (AUPRC) as the primary evaluation metric. A practically important anomaly detection project and strong IEEE data mining project for MTech CSE students focusing on financial data mining, with Python source code and complete IEEE 2026 base paper. | Anomaly Detection | Python · scikit-learn · Pandas · Matplotlib · Kaggle CC Fraud Dataset |
Sequential Pattern Mining Projects
PrefixSpan · SPADE · GSP · Time-Ordered Pattern Mining · Purchase Sequence · E-Health Event Sequences · Log Analytics
| # | IEEE 2026 Data Mining Project Title — Sequential Pattern Mining | Domain | Tools Used |
|---|---|---|---|
| 13 | Temporal Sequential Pattern Mining for Patient Treatment Pathway Discovery in Clinical Event Sequence Data — applies PrefixSpan sequential pattern mining on timestamped clinical event sequences from hospital EHR data to discover common treatment pathways for chronic disease patients, identify clinically deviant sequences associated with adverse outcomes and visualise pathway networks for clinical decision support. A medically impactful and algorithmically sophisticated sequential pattern mining project and premium IEEE data mining project for MTech scholars, with Python implementation and IEEE Transactions on Knowledge and Data Engineering 2026 base paper. | Sequential MiningHealthcare | Python · PySpark · SPMF Library · Pandas · NetworkX |
Privacy-Preserving Data Mining Projects
k-Anonymity · l-Diversity · t-Closeness · Data Perturbation · Differential Privacy · Federated Mining · Anonymisation Techniques
| # | IEEE 2026 Data Mining Project Title — Privacy-Preserving Mining | Domain | Tools Used |
|---|---|---|---|
| 14 | Privacy-Preserving Medical Data Mining Using k-Anonymity and l-Diversity Generalisation and Suppression on Patient Records — implements k-anonymity and l-diversity data anonymisation techniques on a UCI medical dataset, measures information loss using Normalised Certainty Penalty (NCP), and validates that classification data mining models trained on anonymised data achieve acceptable accuracy loss compared to original data. A rigorous and increasingly relevant privacy-preserving data mining project for MTech data mining research projects — addresses a critical gap between utility and privacy in health data sharing, with IEEE 2026 base paper and Python implementation. | Privacy MiningHealthcare | Python · ARX Anonymisation Tool · Pandas · scikit-learn |
Distributed Data Mining Projects
Hadoop MapReduce Mining · PySpark MLlib · Distributed Clustering · Parallel Apriori · Scalable Classification · Big Data Mining
| # | IEEE 2026 Data Mining Project Title — Distributed Data Mining | Domain | Tools Used |
|---|---|---|---|
| 15 | Scalable Distributed Data Mining for Large-Scale E-Commerce Customer Segmentation Using Apache Spark MLlib K-Means — implements parallel K-Means clustering on a 10M+ row retail transaction dataset stored in HDFS using Spark MLlib, comparing convergence time and cluster quality (silhouette score) between single-node scikit-learn and distributed Spark K-Means, and surfacing customer segments in a Power BI dashboard. An MTech-level distributed data mining project bridging big data engineering and data mining — a strong IEEE data mining project with direct connection to both the big data and data mining IEEE literature streams. | Distributed Mining | PySpark · Hadoop HDFS · Spark MLlib · Python · Power BI |
ℹ️ Additional IEEE data mining project topics available on request — including stream data mining using Hoeffding Trees, graph neural network-based data mining, sentiment mining for code-mixed social media, bioinformatics data mining for gene expression, and agricultural yield prediction using data mining. Call or WhatsApp +91 9591912372 with your course, university, preferred algorithm (Apriori / K-Means / SVM / Weka) and submission deadline for a personalised data mining project shortlist.
Need a personalised IEEE 2026 Data Mining Project?
Share your university (VTU / Anna University / JNTU / SRM / Manipal), your preferred domain (association mining, clustering, text mining, healthcare) and your submission deadline — we'll recommend the best IEEE data mining project, confirm delivery timeline and start within 24 hours. Full package: Python / Weka source code, IEEE base paper, preprocessed dataset, report, PPT and viva Q&A support.
Data Mining Project Lab Gallery — Bangalore
A look inside our Bangalore project centre — Python and Weka data mining workstations, algorithm testing rigs, Apriori and K-Means clustering visualisation setups, healthcare data mining analytics benches and student project demo sessions for BE, BTech, MTech, MCA and CSE/IT students working on data mining final year projects.
Business Intelligence Dashboard Lab
CSE Data Analytics Final Year Project
IEEE Data Analytics Project Exhibition
Real-Time Stream Analytics Projects
Big Data Analytics Projects
Healthcare Analytics Projects
Social Media & Sentiment Analytics
MTech Cloud Data Analytics ResearchFrequently Asked Questions — IEEE Data Mining Projects
What are the best IEEE data mining project ideas for CSE final year students in 2026?
Top IEEE data mining projects for CSE final year 2026 include: (1) market basket analysis using Apriori and FP-Growth for retail cross-sell recommendation; (2) classification data mining for diabetes prediction comparing Decision Tree, SVM, Naive Bayes, Random Forest and XGBoost on PIMA Indian dataset using Weka and Python; (3) customer segmentation using K-Means and DBSCAN clustering with RFM features; (4) web usage mining for personalised content recommendation using PrefixSpan; (5) text mining for aspect-level opinion mining from product reviews using LDA and VADER; (6) healthcare data mining for heart disease prediction with SMOTE and ensemble classification; (7) educational data mining on OULAD LMS dataset for at-risk student early warning; (8) social network mining for community detection using Girvan-Newman; (9) anomaly detection data mining for fraud detection using Isolation Forest; and (10) privacy-preserving data mining using k-anonymity on medical records.
Which tools and algorithms are used in data mining final year projects?
Data mining final year projects use Python with scikit-learn (classification, clustering, anomaly detection), mlxtend (Apriori, FP-Growth association mining), Pandas and NumPy for data preprocessing, NLTK and SpaCy for text mining projects, NetworkX for social network mining, Gensim for LDA topic modelling, imbalanced-learn (SMOTE) for handling class imbalance, and Matplotlib / Seaborn for result visualisation. Weka (Java GUI tool) is used for academic data mining projects including J48 decision tree, Naive Bayes, KNN, SMO-SVM, K-Means and DBSCAN with .arff file format datasets. For distributed data mining projects, Apache Spark MLlib and Hadoop MapReduce are used. R with the arules and arulesViz packages is popular for association rule mining projects.
Do you provide data mining projects with Weka source code and Python scripts?
Yes. Every IEEE data mining project with source code from our Bangalore centre includes: complete Python source code (.py files) with scikit-learn / mlxtend / NLTK / NetworkX implementations and inline comments, Weka .arff dataset files and Weka experiment configuration (where applicable), IEEE 2026 base paper (DOI-linked from IEEE Xplore), preprocessed dataset with documentation, algorithm result screenshots and confusion matrices, university-format project report (VTU / Anna University / JNTU / SRM / Manipal), PPT presentation and a comprehensive viva Q&A document covering data mining theory, algorithm selection rationale and future scope discussion.
What is the difference between data mining and machine learning projects?
Data mining is the process of discovering previously unknown, valid and actionable patterns and associations from large pre-existing datasets using statistical and algorithmic techniques — encompassing association rule mining, classification, clustering, anomaly detection, sequential pattern mining and regression. Machine learning builds computational models from training examples that generalise to unseen data. In practice, most data mining projects use machine learning classification algorithms (Random Forest, SVM, XGBoost) as their core technique, making the two fields complementary. IEEE data mining projects have a stronger emphasis on the knowledge discovery process, interpretability and domain-specific pattern extraction — rather than pure prediction accuracy — and are broadly accepted for all Indian university CSE and IT final year project requirements.
Which universities accept IEEE data mining projects for final year CSE and IT students?
Our IEEE data mining projects are designed and documented for all major Indian universities: VTU (Visvesvaraya Technological University), Anna University and its affiliated colleges, JNTU Hyderabad / Kakinada / Anantapur, Osmania University, University of Mysore, Bangalore University, RGUKT, SRM University, Manipal Academy, Amrita University, Shivaji University, Savitribai Phule Pune University, RGPV, Rajasthan Technical University, and all NIT and autonomous college programs across Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, Maharashtra, Kerala and Rajasthan — with university-specific report formatting and IEEE citation style on request.