The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.

Since the previous edition’s publication, great advances have been made in the field of data mining. Not only does this Third Edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology; mining stream; mining social networks; and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today’s most powerful data mining techniques.

ABOUT THE AUTHOR:

Jiawei Han has many years experience working in the field of Data Mining and Database. He has been Editor in Chief of ACM Transactions on Knowledge Discovery from Data. He has also served on the editorial board of many technology journals. The honors he has received include the 2004 ACM SIGKDD Innovations Award. Han is currently a Professor in the Department of Computer Science at the University of Illinois.

Micheline Kamber graduated from Concordia University with a Master's Degree in Computer Science. She is a researcher who also has a passion for writing on technical subjects in a way that is easy to understand.