CS454

DATA WAREHOUSING AND DATA MINING

Objectives

  • 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.

 

Outcomes

  • 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.  

 

TEXT BOOKS

  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.

 

REFERENCE

  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.