Shi, Yong; Tian, Yingjie; Kou, Gang; Peng, Yi; Li, Jianping Optimization based data mining. Theory and applications. (English) Zbl 1216.68014 Advanced Information and Knowledge Processing. New York, NY: Springer (ISBN 978-0-85729-503-3/hbk; 978-0-85729-504-0/ebook). xv, 316 p. (2011). Publisher’s description: 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.This book 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. Cited in 5 Documents MSC: 68-02 Research exposition (monographs, survey articles) pertaining to computer science 90-02 Research exposition (monographs, survey articles) pertaining to operations research and mathematical programming 68T05 Learning and adaptive systems in artificial intelligence 68U99 Computing methodologies and applications 90C05 Linear programming 90C20 Quadratic programming Keywords:support vector machines for classification problems; LOO bounds for support vector machines; multi-class classification problems; unsupervised and semi-supervised support vector machines; feature selection via \(l_p\)-norm support vector machines; multiple criteria linear programming; MCLP extensions; multiple criteria quadratic programming; non-additive MCLP; MC2LP; applications PDFBibTeX XMLCite \textit{Y. Shi} et al., Optimization based data mining. Theory and applications. New York, NY: Springer (2011; Zbl 1216.68014) Full Text: DOI