Introduction -- Data warehousing basic concepts -- Designing data warehouses -- Partitioning and parallelism in data warehouses -- Overview of data mining -- Knowledge representation and knowledge discovery -- Data mining techniques -- Machine learning using classification -- Machine learning using clustering -- Association rules.

Includes bibliographical references and index.

"This unique compendium elaborates the basic perceptions of data warehouses and data mining. The former part of the book covers concepts like introduction to data warehouses, the need for using such data warehouses and key terminologies used in this framework. The latter part of the book covers the data mining concepts and the data mining techniques used in various applications and also explains the machine learning techniques in detail with suitable examples wherever essential. The book is written in simple English and is user-friendly. Each chapter is modeled with several sample scenarios and illustrations wherever necessary. The complete contents of each chapter include chapter technical content, summary, key points to remember, and few case studies for class group discussions and problem-solving. This volume clearly benefits professionals, academicians, data analysts, machine-learning community, undergraduate and postgraduate students"-- Publisher's website.

System requirements: Adobe Acrobat reader.

Mode of access: World Wide Web.