Synopsis
Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. "Data Mining: Concepts and Techniques" equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases. Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as 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. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. "Data Mining: Concepts and Techniques" is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms. The classroom features that are available online include: instructor's manual - course slides (in PowerPoint) - course supplementary readings - sample assignments and course projects. It offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. It is organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn. It presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. It provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis. It addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.
About the Author
Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery. Micheline Kamber is a researcher and freelance technical writer with an M.S. in Computer Science (Artificial Intelligence). She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.
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