Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Jiawei Han, Micheline Kamber, Jian Pei
Language: English
Published by Morgan Kaufmann, 2011
- Hardcover
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[ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: SOME ] [ Writing: NONE ] [ Edition: third ] Publisher: Morgan Kaufmann Pub Date: 7/29/2011 Binding: Hardcover Pages: 696 third edition.
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- Title
- Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
- Author
- Jiawei Han, Micheline Kamber, Jian Pei
- Publisher
- Morgan Kaufmann
- Publication year
- 2011
- Condition
- Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0123814790
- ISBN 13
- 9780123814791
- Edition
- 3rd Edition
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
- Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects
- 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
- Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
"Synopsis" may belong to another edition of this title.
About the Author
Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science (specializing in artificial intelligence) from Concordia University, Canada.
Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his “contributions to the foundation, methodology and applications of data mining and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his “contributions to data mining and knowledge discovery. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences.
"About the title" may belong to another edition of this title.
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