mca Syllabus

Data Mining & Data Warehousing
Code: PGCS105C
Weekly Contact Hour: 3L
Credit: 3

Course Contents
Introduction, Data warehousing and OLAP, Overview of mining operations, Decision tree classifiers, Instance-based learners, Bayesian classifiers, Learning hyper planes, Meta learning, Classifier evaluation, KDD Cup Case study, Clustering, Active learning, Duplicate elimination, Similarity functions, Min hash, Set joins, Sequence mining, Hidden Markov Models, Collaborative Filtering, Association rule mining, Surprising item set mining, Temporal itemset mining, Feature selection methods, Intrusion detection, Forecasting.

Books
1. Pattern recognition and machine learning by Christopher Bishop
2. T. Mitchell. Machine Learning. McGraw-Hill, 1997.
3. Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
4. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers
5. Applied Multivariate statistical analysis by Johnson and Wichern, 3rd Edition, PHI
6. Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
7. Boyd and Vandenberghe Convex optimization Book available online: Local copy
8. K. Jain and R. C. Dubes. Algorithms for Clustering Data. PEARSON Education.