Course Contents:
Module 1: Introduction to data warehouse and multi-dimensional data [6L]
Introduction to Data Warehousing, Data warehouse Architecture and Infrastructure, Data cube and lattice structure. Star, Snowflakes and Fact Constellation models, Components. Building a Data warehouse – Mapping the Data Warehouse to a Multiprocessor Architecture, Reporting and Query tools and Applications, Tool Categories.
Module 2: Online Analytical Processing (OLAP) tools [6L]
Online Analytical Processing (OLAP) vs OLTP, Need –Multidimensional Data Model – OLAP Guidelines - ROLAP vs MOLAP vs HOLAP - Multidimensional versus Multirelational OLAP - Categories of Tools – OLAP Tools and the Internet.
Module 3: Data Mining and Knowledge Discovery Process [7L]
Introduction to Data Mining, Types of data, AI vs ML vs DL - Data Mining Functionalities, Data Mining Systems and Task Primitives - Integration of a Data Mining System with a Data Warehouse - Data Preprocessing, Data Mining vs. Machine learning, Prediction with Regression - Mining Frequent Patterns, Associations and Correlations - Mining Methods (Apriori Algorithm) - Mining Methods-FP Growth Algorithm.
Module 4: Supervised and Unsupervised learning [16L]
Classification and Prediction - Basic Concepts - Decision Tree Induction - Bayesian Classification - Lazy Learners (KNN Classification) - Classification by Backpropagation - Support Vector Machines - Clustering and Applications and Trends in Data Mining - Categorization of Major Clustering Methods, Types of Data - Partitioning Methods - K-Means Clustering - K-Medoids Clustering - Density-Based Methods->DBSCAN - Hierarchical Methods (Agglomerative approach) - Hierarchical Methods (Divisive approach) - Grid Based Methods - Model-Based Clustering Methods.
Module 5: Data mining and Its Applications [5L]
Clustering High Dimensional Data - Outlier Analysis - Data Warehousing Applications - Data Mining Applications - Machine Learning Applications Towards Research.
Text Books:
1. Jiawei Han and MichelineKamber, “Data Mining Concepts and Techniques”, Second
Edition, Elsevier, 2007.
2. Pang‐Ning Tan, Michael Steinbach and Vipin Kumar, “ Introduction To Data
Mining”,Person Education, 2007.
Reference Books:
1. Daniel T.Larose, “Data Mining Methods and Models”, Wile‐Interscience, 2006.
2. Margaret H. Dunham, "Data Mining: Introductory and Advanced Topics", Prentice Hall,
2003.