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Key Features
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
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About the Book
Highly anticipated third edition of the definitive text on data mining.
FEATURES:
Revisions incorporate input from readers, changes in the field, and more material on statistics and machine learning.
Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects.
Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining.
This is the third edition of the premier textbook on the subject of Data Mining, expanding and updating the original. This was the first (and is still the best and most popular) of its kind. Like the previous edition, Data Mining: Concepts and Techniques, 3rd Edition equips students with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases.
Revisions incorporate input from instructors, changes in the field, and more material on statistics and machine learning. Written expressly for database students, this book begins with a conceptual introduction followed by a comprehensive and state-of-the-art coverage of concepts and techniques. 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. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability.
PEDAGOGY AND SUPPLEMENTS:
Extensive references; in-line exercises with sample solutions; ample schematic figures to illustrate concepts; large selection of class-tested supplementary problems (with solutions manual for instructors, as well as lecture slides and a complete set of illustrations from the book).Approx. 150 illustrations
Readership
Data warehouse engineers, data mining professionals, database researchers, statisticians, data analysts, data modelers, and other data professionals working on data mining at the R&D and implementation levels. And upper-level undergrads and graduate studentsin data mining at computer science programs.
Quotes
"[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
Content
View Table of Contents
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