Data Mining. Concepts and Techniques (Morgan Kaufmann Series in Data Management Systems): Concepts and Techniques (Morgan Kaufmann Series in Data Management Systems)
Language: English
Published by Morgan Kaufmann, 2006
Series: Book 39 of 52 - The Morgan Kaufmann Series in Data Management Systems
- Hardcover
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- Title
- Data Mining. Concepts and Techniques (Morgan Kaufmann Series in Data Management Systems): Concepts and Techniques (Morgan Kaufmann Series in Data Management Systems)
- Author
- Jiawei Han, Micheline Kamber
- Publisher
- Morgan Kaufmann
- Publication year
- 2006
- Condition
- good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1558609016
- ISBN 13
- 9781558609013
- Edition
- 2nd Edition
- Series
- Book 39 of 52: The Morgan Kaufmann Series in Data Management Systems
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.
- 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
"Synopsis" may belong to another edition of this title.
About the Author
Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his “contributions to the foundation, methodology and applications of data mining and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his “contributions to data mining and knowledge discovery. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences.
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.
"About the title" may belong to another edition of this title.
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