Data Mining: Concepts and Techniques / Edition 2

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Overview

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.

Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
• A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
• Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
• Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
• Complete classroom support for instructors at www.mkp.com/datamining2e companion site.

"...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

Data Warehouse and OLAP Technology; Data Preprocessing; Data Mining Primitives, Languages, and System Architectures; Concept Description: Characterization and Comparison; Mining Association Rules in Large Databases; Classification and Prediction; Cluster Analysis; Mining Time-Series, Sequence, and Stream Data; Mining Spatial, Multimedia, and Biological Databases; Text Mining and Web Mining; Visual and Audio Data Mining

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