Optimization Based Data Mining: Theory and Applications
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) ha
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Yong Shi Yingjie Tian Gang Kou Yi Peng Jianping Li
Optimization Based Data Mining: Theory and Applications
Yong Shi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China [email protected] and College of Information Science & Technology University of Nebraska at Omaha Omaha, NE 68182 USA [email protected] Yingjie Tian Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China [email protected]
Gang Kou School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 China [email protected] Yi Peng School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 China [email protected] Jianping Li Institute of Policy and Management Chinese Academy of Sciences Beijing 100190 China [email protected]
ISSN 1610-3947 ISBN 978-0-85729-503-3 e-ISBN 978-0-85729-504-0 DOI 10.1007/978-0-85729-504-0 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2011929129 © Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: deblik Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
This book is dedicated to the colleagues and students who have worked with the authors
Preface
The purpose of this book is to provide up-to-date progress both in Multiple Criteria Programming (MCP) and Support Vector Machines (SVMs) that have become powerful tools in the field of data mining. Most of the content in this book are directly from the research and application activities that our research group has conducted over the last ten years. Although the data mining community is familiar with Vapnik’s SVM [206] in classification, using optimization techniques to deal with data separation and data analysis goes back more tha
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