DATA WAREHOUSING AND DATA MINING

Introduction To Data Warehousing -Concept And Characteristics of Data Warehouse, Distributed V/S Centralized Data Warehouses, Design Consideration of Data Warehouse ,Tools For Data Warehousing ,Data Warehouse Models ,Architecture Of Data Warehouse And Organizational Issues ,Managing Security In Data Warehouse Environment.           

OLTP And OLAP Systems And Their Applications, Steps Involved In Project Planning, Management, Project Estimation, Introduction To ROLAP AND MOLAP Application Of Data Warehouse - National Data Warehouses, Census Data, Prices Of Essential Commodities.

Data Mining:   Introduction, Definition, KDD Vs. DM, DBMS Vs. DM, DM Techniques, Issues And challenges In DM, DM Applications. Association Rules: A Prior Algorithm, Partition, Search, Incremental, Border, FP-Tree Growth Algorithms.          

Classification: Parametric And Non-Parametric Technology: Bayesian Classification, Two Class And Generalized Class Classification, Classification Error, Decision Boundary, Discriminate Functions.

Non-Parametric Methods for Classification. Clustering: Hierarchical And Non-Hierarchical Techniques, K-Medoid Algorithm, Partitioning, Clara, Clarans. Advanced Hierarchical Algorithms.  

Decision Trees: Decision Tree Induction, Tree Pruning, Extracting Classification Rules From Decision Trees, Decision Tree Construction Algorithms, Decision Tree Construction With Presorting. Other Techniques For Data Mining: Introduction, Learning, Neural Networks, Data    Mining Using Neural Networks, Genetic Algorithms. Web Mining: Web Mining, Text Mining, Data Mining Tasks.