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Other titles in the Morgan Kaufmann Series in Data Management Systems series:
Data Mining: Concepts and Techniques (Morgan Kaufmann Series in Data Management Systems)by Jiawei Han
Synopses & Reviews
This is the third edition of the premier professional reference on the subject of data mining, expanding and updating the previous market leading edition. This was the first (and is still the best and most popular) of its kind. Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining. Like the first and second editions, Data Mining: Concepts and Techniques, 3rd Edition equips professionals with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases.
The first and second editions also established itself as the market leader for courses in data mining, data analytics, and knowledge discovery. Revisions incorporate input from instructors, changes in the field, and new and important topics such as data warehouse and data cube technology, mining stream data, mining social networks, and mining spatial, multimedia and other complex data. 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. relational data.
-- A comprehensive, practical look at the concepts and techniques you need 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,
-- Scores 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 as well as bonus content available at the companion website
Book News Annotation:
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. No dates are cited for the first two editions. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)
The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.
Since the previous edition’s publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. 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. This is the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges.
* 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
A comprehensive and practical look at the concepts and techniques you need to get the most out of real business data
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. Getting to Know Your Data
Chapter 3. Preprocessing
Chapter 4. Data Warehousing and On-Line Analytical Processing
Chapter 5. Data Cube Technology
Chapter 6. Mining Frequent Patterns, Associations and Correlations: Concepts and Methods
Chapter 7. Advanced Frequent Pattern Mining
Chapter 8. Classification: Basic Concepts
Chapter 9. Classification: Advanced Methods
Chapter 10. Cluster Analysis: Basic Concepts and Methods
Chapter 11. Advanced Cluster Analysis
Chapter 12. Outlier Analysis
Chapter 13. Data Mining Trends and Research Frontiers in Data Mining
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