Data mining and warehousing

This book is intended as a text in data mining and warehousing for engineering and post-graduate
level students.
We have attempted to cover the major topics in data mining and warehousing in depth.
Advanced material, however, has been put into separate chapters so that courses in a variety of
levels can be taught from this book. A brief synopsis of the chapters and comments on their
appropriateness in a basic course is therefore appropriate.
Chapter 1 introduces the basic concepts to data mining and warehousing and its essentials.
Chapter 2 covers the types of knowledge and learning concepts, which are essential to data
mining techniques.
Chapter 3 covers how to discover the knowledge hidden in the databases using various
techniques.
Chapter 4 deals with various types of data mining techniques and algorithms available.
Chapter 5 covers various applications related to data mining techniques and real time usage of
these techniques in various fields like business, commercial organizations, etc.
Chapter 6 deals with the evaluation of data warehouse and need of the new design process
for databases.
Chapter 7 covers how to design the data warehouse using fact tables, dimension tables and
schemas.
Chapter 8 deals with partitioning strategy in software and hardware.
Chapter 9 covers the data mart and meta data.
Chapter 10 covers backup and recovery process.
Chapter 11 deals with performance, tuning of data warehouse, challenges, benefits and new
architecture of the data warehouse.
This book also contains sufficient material to make up an advanced course on data mining and
warehousing. Each and every chapter has exercise. Model question paper is also given. We conclude
that, this book is the only book, which deals both data mining techniques and data warehousing
concepts suited for the modern time.