Data Warehousing and Data Mining

Contents: 1. Evolution of Decision Support Systems and Data Warehousing. 2. From Data to Information. 3. Data Warehouse Architecture and OLAP Servers. 4. Defining the Business Requirements. 5. Data Warehouse Environment. 6. Data Warehouse Design. 7. Data Warehouse Schema. 8. Case Studies. 9. Introduction to Data Mining. 10. Understanding Data and Data Preprocessing. 11. Frequent Pattern Mining. 12. Classification. 13. Clustering. 14. A Brief Overview of Outlier Detection Techniques. 15. Introduction to Web, Temporal and Spatial Mining.

Divided in two sections, this book is organized in 15 chapters. The first section covers data warehousing concepts and the steps required in creating a data warehouse for a decision support system along with data warehouse implementation case study. The second section provides a comprehensive introduction to data mining and is designed to be accessible and useful to students, instructors, researchers and professionals. It includes data preprocessing, visualization, predictive modeling, association analysis, clustering, and anomaly detection. The goal is to present fundamental concepts and algorithms for each topic, thus providing reader with the necessary background for the application of data mining to the real problems.