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Data Mining : Concepts and Techniques (2ND 06 - Old Edition)by Jiawei Han
Synopses & ReviewsPlease note that used books may not include additional media (study guides, CDs, DVDs, solutions manuals, etc.) as described in the publisher comments.
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.
Characterization and Comparison
Chapter 6: Mining Association Rules in Large Databases
Chapter 7: Classification and Prediction
Chapter 8: Cluster Analysis
Chapter 9: Mining Time-Series, Sequence, and Stream Data
Chapter 10: Mining Spatial, Multimedia, and Biological Databases
Chapter 11: Text Mining and Web Mining
Chapter 12: Visual and Audio Data Mining
Chapter 13: Data Mining Applications and Trends in Data Mining
This is the 2nd edn of the premier professional reference on the subject of Data Mining, expanding and updating the original. Combines sound theory with truly practical applications to prepare students for real-world challenges in the professional database field. Includes approximately 100 pages of new material. The resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. This equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases.
About 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.
Simon Fraser University, Burnaby, Canada
Table of Contents
Chapter 1: Introduction
Chapter 2: Data Warehouse and OLAP Technology for Data Mining
Chapter 3: Data Preprocessing
Chapter 4: Data Mining Primitives, Languages, and System Architectures
Chapter 5: Concept
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