Data Mining for Business Applications [Cao, Yu, Zhang & Zhang 2008-10-09](1).pdf

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Data Mining for
Business Applications
Edited by
Longbing Cao
Philip S. Yu
Chengqi Zhang
Huaifeng Zhang
13
Editors
Longbing Cao
School of Software
Faculty of Engineering and
Information Technology
University of Technology, Sydney
PO Box 123
Broadway NSW 2007, Australia
lbcao@it.uts.edu.au
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
chengqi@it.uts.edu.au
Philip S.Yu
Department of Computer Science
University of Illinois at Chicago
851 S. Morgan St.
Chicago, IL 60607
psyu@cs.uic.edu
Huaifeng Zhang
School of Software
Faculty of Engineering and
Information Technology
University of Technology, Sydney
PO Box 123
Broadway NSW 2007, Australia
hfzhang@it.uts.edu.au
ISBN: 978-0-387-79419-8
DOI: 10.1007/978-0-387-79420-4
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 soft-
ware, 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 up-
coming 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 knowl-
edge 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 ad-
dressed 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 (6I
s
), namely
human
roles and intelligence, domain knowledge and intelligence, network and web intel-
ligence, organizational and social intelligence, in-depth data intelligence,
and most
importantly, the
metasynthesis of the above intelligences.
It is certainly not our ambition to cover everything of the 6I
s
in this book. Rather,
this edited book features the latest methodological, technical and practical progress
on promoting the successful use of data mining in a collection of business domains.
The book consists of two parts, one on AKD methodologies and the other on novel
AKD domains in business use.
In Part I, the book reports attempts and efforts in developing domain-driven
workable AKD methodologies. This includes domain-driven data mining, post-
processing rules for actions, domain-driven customer analytics, roles of human in-
telligence in AKD, maximal pattern-based cluster, and ontology mining.
Part II selects a large number of novel KDD domains and the corresponding
techniques. This involves great efforts to develop effective techniques and tools for
emergent areas and domains, including mining social security data, community se-
curity data, gene sequences, mental health information, traditional Chinese medicine
data, cancer related data, blog data, sentiment information, web data, procedures,
v
vi
Preface
moving object trajectories, land use mapping, higher education, flight scheduling,
and algorithmic asset management.
The intended audience of this book will mainly consist of researchers, research
students and practitioners in data mining and knowledge discovery. The book is
also of interest to researchers and industrial practitioners in areas such as knowl-
edge engineering, human-computer interaction, artificial intelligence, intelligent in-
formation processing, decision support systems, knowledge management, and AKD
project management.
Readers who are interested in actionable knowledge discovery in the real world,
please also refer to our monograph:
Domain Driven Data Mining,
which has been
scheduled to be published by Springer in 2009. The monograph will present our re-
search outcomes on theoretical and technical issues in real-world actionable knowl-
edge discovery, as well as working examples in financial data mining and social
security mining.
We would like to convey our appreciation to all contributors including the ac-
cepted chapters’ authors, and many other participants who submitted their chapters
that cannot be included in the book due to space limits. Our special thanks to Ms.
Melissa Fearon and Ms. Valerie Schofield from Springer US for their kind support
and great efforts in bringing the book to fruition. In addition, we also appreciate all
reviewers, and Ms. Shanshan Wu’s assistance in formatting the book.
Longbing Cao, Philip S.Yu, Chengqi Zhang, Huaifeng Zhang
July 2008

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