• To understand the principles of Data warehousing and Data Mining.
  • To know the Architecture of a Data Mining system and Data preprocessing Methods.
  • To perform classification and prediction of data.



  • Technical knowhow of the Data Mining principles and techniques for real time applications


Unit – I

Introduction  - Relation To Statistics,  Databases- Data Mining Functionalities-Steps In Data Mining Process-Architecture Of A Typical Data Mining Systems


Unit – II

Data Preprocessing and Association Rules-Data Cleaning, Integration, Transformation, Reduction, Discretization Concept Hierarchies-Data Generalization And Summarization


Unit – III

Predictive Modeling - Classification And Prediction-Classification By Decision Tree Induction-Bayesian Classification-Prediction-Clusters Analysis: Categorization Of Major Clustering Methods: Partitioning Methods - Hierarchical Methods


Unit – IV

Data Warehousing Components -Multi Dimensional Data Model- Data Warehouse Architecture-Data Warehouse Implementation-Mapping The Data Warehouse To Multiprocessor Architecture- OLAP.


Unit – V

Applications of Data Mining-Social Impacts Of Data Mining-Tools-WWW-Mining Text Database-Mining Spatial Databases.  



  1. Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufmann Publishers, 2002.
  2. Alex Berson and Stephen J. Smith, “Data Warehousing, Data Mining, & OLAP”, Tata McGraw- Hill, 2004.



  1. Usama M. Fayyad, Gregory Piatetsky - Shapiro, Padhrai Smyth, and Ramasamy Uthurusamy, "Advances In Knowledge Discovery And Data Mining", The M.I.T Press, 1996.   
  2. Ralph Kimball, "The Data Warehouse Life Cycle Toolkit", John Wiley & Sons Inc., 1998.
  3. Sean Kelly, "Data Warehousing In Action", John Wiley & Sons Inc., 1997.