(CS397 until new course number is approved)

A course that introduces the concepts, techniques, and systems of Data  Warehousing and Data Mining. Offered yearly.

It covers the following topics: (1) introduction, (2) concepts and design of data warehouses, (3) implementation of data warehouse and OLAP systems, (4) data preprocessing, (5) primitives, languages, and systems architectures for data warehousing and data mining, (6) concept description, (7) association analysis, (8) classification and prediction, (9) cluster analysis, (10) mining complex types of data, (11) data mining applications and trends on data mining.

1. Introduction to data mining and warehousing
2. Concepts and design of data warehouse 4 hours
3. Implementation of data warehouse and OLAP systems
4. Data preprocessing
5. Data mining primitives, languages, and system architectures
6. Concept description
7. Mining association rules in large databases
? Midterm exam
8. Classification and prediction
9. Cluster analysis
10. Mining complex types of data
11. Applications and trends in data mining


Required textbooks:
Jiawei Han and Micheline Kamber: Data Mining--Concepts and Techniques, Morgan Kaufmann, 2001

Recommended textbooks:
1. R. Kimball and M. Ross, The Data Warehouse Toolkit, 2ed, John Wiley, 2002.
2. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations,Morgan Kaufmann, 2001.