UNIT I: INTRODUCTION 
Data Mining-motivation, importance-DM Functionalities, Basic Data Mining Tasks, DM Vs KDD,DM Metrics, DM Applications, Social implications.
UNIT II: DATA WAREHOUSING 
Difference between Operational Database and Data warehouse-Multidimensional Data Model: From tables to data Cubes, Schemas, Measures-DW Architecture: Steps for design and construction of DW, 3-tier DW Architecture-DW Implementation: Efficient computation of DATA Cubes, Efficient Processing of OLAP queries, Metadata repository.
UNIT III : DATA PREPROCESSING, DATA MINING PRIMITIVES,LANGUAGES 
Data cleaning, Data Integration and Transformation, Data Reduction. Discretization and concept Hierarchy Generation. Task-relevant data, Background Knowledge, Presentation and Visualization of Discovered Patterns. Data Mining Query Language-other languages for data mining
UNIT lV: DATA MINING ALGORITHMS 
Association Rule Mining: MBA Analysis, The Apriori Algorithm, Improving the efficiency of Apriori. Mining Multidimensional Association rules from RDBMS and DXV. Classification and Predication: Decision Tree, Bayesian Classification back propagation, Cluster Analysis: Partitioning Methods, Hierarchical Method, Grid-based methods, Outlier Analysis.
UNIT V: WEB, TEMPORAL AND SPATIAL DATA MINING 
Web content Mining, Web Structure Mining, Web usage mining. Spatial Mining: Spatial DM primitives, Generalization and Specialization, Spatial rules, spatial classification and clustering algorithms. Temporal Mining: Modeling Temporal Events, Times series, Pattern Detection, Sequences.
1.Jiawei I-lan, & Micheline kamber,"data Mining: Concepts and Techniques". Harcourt India Private Limited, First Indian Reprint,2001
2.Margaret H.Dunham,"Data Mining: Introductory and Advanced Topics".Pearson Education,First Indian Reprint,2003
3.Arun K. Pujari," Data Mining Techniques", University Press (India ) Limited, First Edition,2001
4.Efrem O. Mallach, "Decision Support and Data Warehouse Systems", Mcgraw-Hill International Edition,2000