Introduction : Data warehousing – definitions and characteristics, Multi-dimensional data model,
Warehouse schema.
Data Marts: Data marts, types of data marts, loading a data mart, metadata, data model,
maintenance, nature of data, software components; external data, reference data, performance issues,
monitoring requirements and security in a data mart.
Online Analytical Processing [4L] : OLTP and OLAP systems, Data Modeling, LAP tools, State of the
market, Arbor Essbase web, Microstrategy DSS web, Brio Technology, star schema for multi dimensional
view, snowflake schema; OLAP tools.
Developing a Data Warehousing : Building of a Data Warehousing, Architectural strategies &
organizational issues, design considerations, data content, distribution of data, Tools for Data Warehousing
Data Mining : Definitions; KDD(Knowledge Discovery database) versus Data Mining; DBMS versus
Data Mining, Data Mining Techniques; Issues and challenges; Applications of Data Warehousing & Data
mining in Government.
Association Rules : A priori algorithm, Partition algorithm, Dynamic inset counting algorithm, FP –
tree growth algorithm; Generalized association rule.
Clustering Techniques [4L] : Clustering paradigm, Partition algorithms, CLARA, CLARANS;
Hierarchical clustering, DBSCAN, BIRCH, CURE; Categorical clustering, STIRR, ROCK, CACTUS.
Decision Trees: Tree construction principle, Best split, Splitting indices, Splitting criteria, Decision
tree construction with presorting.
Web Mining : Web content Mining, Web structure Mining, Web usage Mining, Text Mining.
Temporal and Spatial Data Mining [5L] : Basic concepts of temporal data Mining, The GSP algorithm,
SPADE, SPIRIT, WUM.