- Hardcover: 744 pages
- Publisher: Morgan Kaufmann; 3 edition (25 July 2011)
- Language: English
- ISBN-10: 9380931913
- ISBN-13: 978-9380931913
- ASIN: 0123814790
- Product Dimensions: 19.3 x 3.8 x 23.9 cm
- Average Customer Review: 1 customer review
- Amazon Bestsellers Rank: #37,366 in Books (See Top 100 in Books)
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Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) Hardcover – 25 Jul 2011
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"A well-written textbook (2nd ed., 2006; 1st ed., 2001) on data mining or knowledge discovery. The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. Summing Up: Highly recommended. Upper-division undergraduates through professionals/practitioners." --CHOICE
"This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers." --ACM’s Computing Reviews.com
"We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses." --Gregory Piatetsky, President, KDnuggets
"Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines)…. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book." --From the foreword by Christos Faloutsos, Carnegie Mellon University
"A very good textbook on data mining, this third edition reflects the changes that are occurring in the data mining field. It adds cited material from about 2006, a new section on visualization, and pattern mining with the more recent cluster methods. It’s a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge…Two additional items are worthy of note: the text’s bibliography is an excellent reference list for mining research; and the index is very complete, which makes it easy to locate information. Also, researchers and analysts from other disciplines--for example, epidemiologists, financial analysts, and psychometric researchers--may find the material very useful." --Computing Reviews
"Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both computer science, Simon Fraser U., British Columbia) present a textbook for an advanced undergraduate or beginning graduate course introducing data mining. Students should have some background in statistics, database systems, and machine learning and some experience programming. Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers. Chapter-end exercises are included." --SciTech Book News
"This book is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book’s coverage of underlying concepts. A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification. The final chapter describes the current state of data mining research and active research areas." --BCS.org
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.
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It's usable though, much more convenient in electronic form and I've saved myself about $50 by renting the book on Amazon for four months compared to buying it at the Uni bookshop.
Get the hardback version instead.
The author(s) did a great job of explaining complex topics. It's a textbook, so there's a good combination of both theory and math. I like the way that the book uses one company as the focus of examples for the entire book. It's creates continuity. I will admit that there are a few points where the author assumes that the reader knows a math concept and doesn't explain it, but overall, if you have at least an introductory level knowledge of statistics, you'll be fine. .