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FRINGEINDEPENEDENTREVIEW
Posted September 24, 2011
Are you a computer science student, application developer, and business professional; as well as, a researcher? If you are, then this book is for you! Authors Jiawei Han, Micheline Kamber, and Jian Pei, have done an outstanding job of writing a third edition of a book which explores the concepts and techniques of knowledge discovery and data mining. Han, Kamber and Pei, begin by providing an introduction to the multidisciplinary field of data mining. In addition, the authors introduce the general data features. The authors then focus on the techniques for data processing. Then, they look at the basic concepts, modeling, design architectures, and general implementations of data warehouses and OLAP; as well as, the relationship between data warehousing and other data generalization methods. Next, the authors take an in-depth look at cube technology, presenting a detailed study of methods of data cube computation, including Star-Cubing and high-dimensional OLAP methods. They continue with an in-depth look at the fundamental concepts, such as market basket analysis, with many techniques for frequent itemset mining presented in an organized way. In addition, the authors discuss methods for pattern mining in multilevel and multidimensional space, mining rare and negative patterns, mining colossal patterns and high-dimensional data, constraint-based pattern mining, and mining compressed or approximate patterns. The authors then introduce the basic concepts and methods for classification, including decision tree induction, Bayes classification, and rule-based classification. Then, they discuss advanced methods for classification, including Bayesian belief networks, the neural network technique of backpropagation , support vector machines, classification using frequent patterns, k-nearest-neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Next, the authors introduce the basic concepts and methods for data clustering, including an overview of basic cluster analysis methods, partitioning methods, hierarchical methods, density-based methods, and grid-based methods. They continue with a discussion of advanced methods for clustering, including probabilistic model-based clustering, clustering high-dimensional data, clustering graph and network data, and clustering with constraints. In addition, the authors introduce the basic concepts of outliers and outlier analysis, and discuss various outlier detection methods from the view of degree of supervision; as well as, from the view of approaches. Finally, the authors discuss trends, applications, and research frontiers in data mining. This most excellent book is not intended as an introduction to statistics, machine learning, database systems, or other such areas. Rather, the book is a comprehensive introduction to data mining.
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Overview
"...each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms & sound implementations ready to be used directly or with strategic modification against live data."
Editorial Reviews
From the Publisher
"[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
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Meet 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 Associate Professor of Computing Science and the director of Collaborative Research and Industry Relations at the School of Computing Science at Simon Fraser University, Canada. In 2002-2004, he was an Assistant Professor of Computer Science and Engineering at the State University of New York (SUNY) at Buffalo. He received a Ph.D. degree in Computing Science from Simon Fraser University in 2002, under Dr. Jiawei Han's supervision.
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