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.
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