Data Mining for Business Applications
Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centere
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Data Mining for Business Applications Edited by
Longbing Cao Philip S. Yu Chengqi Zhang Huaifeng Zhang
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Editors Longbing Cao School of Software Faculty of Engineering and Information Technology University of Technology, Sydney PO Box 123 Broadway NSW 2007, Australia [email protected]
Philip S.Yu Department of Computer Science University of Illinois at Chicago 851 S. Morgan St. Chicago, IL 60607 [email protected]
Chengqi Zhang Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology, Sydney PO Box 123 Broadway NSW 2007, Australia [email protected]
ISBN: 978-0-387-79419-8 DOI: 10.1007/978-0-387-79420-4
Huaifeng Zhang School of Software Faculty of Engineering and Information Technology University of Technology, Sydney PO Box 123 Broadway NSW 2007, Australia [email protected]
e-ISBN: 978-0-387-79420-4
Library of Congress Control Number: 2008933446 ¤ 2009 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com
Preface
This edited book, Data Mining for Business Applications, together with an upcoming monograph also by Springer, Domain Driven Data Mining, aims to present a full picture of the state-of-the-art research and development of actionable knowledge discovery (AKD) in real-world businesses and applications. The book is triggered by ubiquitous applications of data mining and knowledge discovery (KDD for short), and the real-world challenges and complexities to the current KDD methodologies and techniques. As we have seen, and as is often addressed by panelists of SIGKDD and ICDM conferences, even though thousands of algorithms and methods have been published, very few of them have been validated in business use. A major reason for the above situation, we believe, is the gap between academia and businesses, and the gap between academic research and real business needs. Ubiquitous challenges and complexities from the real-world complex problems can be categorized by the involvement of six types of intelligence (6Is ), namely human roles and intelligence, domain knowledge and intelligence, network and web intelligence, organizational and social intelligence, in-depth data intelligence, and most importantly, the metasynthesis of the above intelligences. It is certainly not our ambition to
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