Data Warehousing and Data Mining

Unit No.

CONTENT

1

Unit 1 ⇗:

• Introduction to Data Warehousing, Concept of Data Warehouse, DBMS verses data warehouse, Data Marts, Metadata, Multidimensional data model, Multidimensional database,  Data warehouse Measures, their categorization and computation, Multi-dimensional database hierarchies.

2

Unit 2 ⇗:

• Data Warehouse Architecture, Operations in OLAP,Advantages of OLAP over OLTP,Three-Tier Data Warehouse architecture, OLAP Guidelines, Multidimensional versus Multirelational OLAP , Categories of Tools, OLAP Tools and the Internet.

3

Introduction to Data Mining ⇗:

• Basic Concepts of Data Mining; Data Mining primitives: Task-relevant data, mining objective, measures and identification of patterns, KDD versus data mining, data mining tools and applications. Data Mining Query Languages: Data specification, specifying kind of knowledge, hierarchy specification, pattern presentation & visualization specification, data mining languages and standardization of data mining, Architectures of Data Mining Systems.

4

Data Mining Techniques ⇗:

• Association rules: Association rules from transaction database & relational database, correlation analysis; Classification and predication using decision tree induction. Introduction to Clustering techniques, partition method, and Hierarchical method.

5

Overview of Advanced Features of Data Mining ⇗:

• Mining complex data objects, Spatial databases, Multimedia databases, Time series and Sequence data; mining Text Databases and mining Word Wide Web.