Fundamentals of Data Mining

5 Units · Textbooks · Resources

Syllabus – Fundamentals of Data Mining

Click on any unit card to access detailed notes, examples, and interactive quizzes.

Unit I

Introduction to Data Mining

Definition, Characteristics, Applications
KDD Process, Data Warehouse Concepts
OLAP vs OLTP, ETL Pipeline
Explore Unit 1 Case Studies Unit 1 Activity
Unit II

Data Preprocessing

Data Cleaning & Integration
Transformation & Reduction
Missing Data, Normalization
Explore Unit 2 Case Studies Unit 2 Activity
Unit III

Classification & Prediction

Decision Trees, Naive Bayes, k-NN
Rule-Based Classifiers
Model Evaluation Metrics
Explore Unit 3 Case Studies Unit 3 Activity
Unit IV

Clustering Techniques

K-Means, Hierarchical Clustering
DBSCAN, Similarity Measures
Cluster Validation
Explore Unit 4 Case Studies Unit 4 Activity
Unit V

Association Rule Mining

Apriori Algorithm
Support, Confidence, Lift
FP-Growth, Market Basket Analysis
Explore Unit 5 Case Studies Unit 5 Activity

Textbooks & Resources

Textbook 1 Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei
Textbook 2 Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, Vipin Kumar
Lab Manual 9 Hands-on Experiments Open Lab Manual
Annexure Glossary & Key Formulas Open Annexure