Data Mining: Concepts and Techniques / Edition 3

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Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases." "Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as 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, keeping your eye on the issues that will affect your project's results and your overall success." "Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.

"...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."

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Editorial Reviews

From the Publisher
"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

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Product Details

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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Read an Excerpt

The Preeminent textbook and professional reference on data mining from the recognized authoirty on the subject.
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Table of Contents

Ch. 1 Introduction 1
Ch. 2 Data preprocessing 47
Ch. 3 Data warehouse and OLAP technology : an overview 105
Ch. 4 Data cube computation and data generalization 157
Ch. 5 Mining frequent patterns, associations, and correlations 227
Ch. 6 Classification and prediction 285
Ch. 7 Cluster analysis 383
Ch. 8 Mining stream, time-series, and sequence data 467
Ch. 9 Graph mining, social network analysis, and multirelational data mining 535
Ch. 10 Mining object, spatial, multimedia, text, and Web data 591
Ch. 11 Applications and trends in data mining 649
App An introduction to Microsoft's OLE DB for data mining 691
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  • 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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