Table of contents
About this book
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.
- Book Title Optimization Based Data Mining: Theory and Applications
- Series Title Advanced Information and Knowledge Processing
- DOI https://doi.org/10.1007/978-0-85729-504-0
- Copyright Information Springer-Verlag London Limited 2011
- Publisher Name Springer, London
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-0-85729-503-3
- Softcover ISBN 978-1-4471-2653-9
- eBook ISBN 978-0-85729-504-0
- Series ISSN 1610-3947
- Edition Number 1
- Number of Pages XVI, 316
- Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
Data Mining and Knowledge Discovery
Input/Output and Data Communications
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