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Data Mining, Southeast Asia Edition: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) Hardcover – 4 Jun 2006

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Product details

  • Hardcover: 800 pages
  • Publisher: Morgan Kaufmann; 2 edition (4 Jun. 2006)
  • Language: English
  • ISBN-10: 1558609016
  • ISBN-13: 978-1558609013
  • Product Dimensions: 25 x 18.5 x 3.8 cm
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 1,230,354 in Books (See Top 100 in Books)
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  • See Complete Table of Contents

Product description

Review

"Now, for the first time, there is an outstanding text on data mining which covers the science as well as the process in a comprehensive manner and with great lucidity." --Laks Lakshmanan, Concordia University, on the 1st ed:

The second edition of Han and Kamber Data Mining: Concepts and Techniques updates and improves the already comprehensive coverage of the first edition and adds coverage of new and important topics, such as mining stream data, mining social networks, and mining spatial, multi-media and other complex data. This book will be an excellent textbook for courses on Data Mining and Knowledge Discovery.Gregory Piatetsky-Shapiro, President, KDnuggets

The second edition is the most complete and up-to-date presentation on this topic. Compared to the already comprehensive and thorough coverage of the first edition it adds the state-of-the-art research results in new topics such as mining stream, time-series and sequence data as well as mining spatial, multimedia, text and web data. This book is a "must have" for all instructors, researchers, developers and users in the area of data mining and knowledge discovery.Hans-Peter Kriegel, University of Munich, Germany

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 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.


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on 28 October 2009
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Amazon.com: 3.6 out of 5 stars 13 reviews
4 people found this helpful.
4.0 out of 5 starsefficient, if technically a bit shallow
on 24 October 2008 - Published on Amazon.com
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5.0 out of 5 starsEverything you want to know about data science is in this book
on 2 January 2017 - Published on Amazon.com
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5.0 out of 5 starsKinda just sits around motionless
on 7 October 2016 - Published on Amazon.com
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3.0 out of 5 starsClass text
on 2 December 2016 - Published on Amazon.com
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2 people found this helpful.
5.0 out of 5 starsGreat book for data mining
on 11 November 2008 - Published on Amazon.com
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