Optimization Based Data Mining: Theory and Applications
Language: English
Published by Springer London, 2011
Series: Book 30 of 66 - Advanced Information and Knowledge Processing
- Hardcover
- New
Condition: New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces MCLP for data mining intuitively, systemically and comprehensivelyOffers classification problems and regression problems which are the two main components of data miningConstructs SVM s for solving multi-class classification prob.
Seller Inventory # 5979330
- Title
- Optimization Based Data Mining: Theory and Applications
- Author
- Yong Shi|Yingjie Tian|Gang Kou|Yi Peng|Jianping Li
- Publisher
- Springer London
- Publication year
- 2011
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 0857295039
- ISBN 13
- 9780857295033
- Series
- Book 30 of 66: Advanced Information and Knowledge Processing
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.
Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.
Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
"Synopsis" may belong to another edition of this title.
From the Back Cover
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.
Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.
Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
"About the title" may belong to another edition of this title.
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