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 IV
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.
Text Book(s)
“Data Mining: Concepts and Techniques” - Jiawei Han, Micheline Kamber Contributor Micheline Kamber Edition: 2, illustrated, revised Published by Morgan Kaufmann, 2006 ISBN 1558609016
References Books
1. “Data Warehousing, Data Mining & OLAP” - Berson & Alex - Tata McGraw Hill Publishing - 2004 - ISBN: 0070587418.
2. “Introduction to Data Mining” - Pang-Hing Tan, Vipin Kumar & Michael Steinbach - Pearson Education - 2007 - ISBN: 9788131714720.