LNAI 3755_ Data Mining_ Theory, Methodology, Techniques, and Applications [Williams & Simoff 2006-04-03](1).pdf

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Lecture Notes in Artificial Intelligence
Edited by J. G. Carbonell and J. Siekmann
3755
Subseries of Lecture Notes in Computer Science
Graham J. Williams Simeon J. Simoff (Eds.)
Data Mining
Theory, Methodology, Techniques,
and Applications
13
Series Editors
Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA
Jörg Siekmann, University of Saarland, Saarbrücken, Germany
Volume Editors
Graham J. Williams
Togaware Data Mining
Canberra, Australia
E-mail: graham.williams@togaware.com
Simeon J. Simoff
University of Technology, Faculty of Information Technology
Sydney Broadway PO Box 123, NSW 2007, Australia
E-mail: simeon@it.uts.edu.au
Library of Congress Control Number: 2006920576
CR Subject Classification (1998): I.2, H.2.8, H.2-3, D.3.3, F.1
LNCS Sublibrary: SL 7 – Artificial Intelligence
ISSN
ISBN-10
ISBN-13
0302-9743
3-540-32547-6 Springer Berlin Heidelberg New York
978-3-540-32547-5 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is
concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting,
reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication
or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965,
in its current version, and permission for use must always be obtained from Springer. Violations are liable
to prosecution under the German Copyright Law.
Springer is a part of Springer Science+Business Media
springer.com
© Springer-Verlag Berlin Heidelberg 2006
Printed in Germany
Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India
Printed on acid-free paper
SPIN: 11677437
06/3142
543210
Preface
Data mining has been an area of considerable research and application in
Australia and the region for many years. This has resulted in the establish-
ment of a strong tradition of academic and industry scholarship, blended with
the pragmatics of practice in the field of data mining and analytics. ID3, See5,
RuleQuest.com, MagnumOpus, and WEKA is but a short list of the data min-
ing tools and technologies that have been developed in Australasia. Data mining
conferences held in Australia have attracted considerable international interest
and involvement.
This book brings together a unique collection of chapters that cover the
breadth and depth of data mining today. This volume provides a snapshot of the
current state of the art in data mining, presenting it both in terms of technical
developments and industry applications. Authors include some of Australia’s
leading researchers and practitioners in data mining, together with chapters
from regional and international authors.
The collection of chapters is based on works presented at the Australasian
Data Mining conference series and industry forums. The original papers were
initially reviewed for the workshops, conferences and forums. Presenting authors
were provided with substantial feedback, both through this initial review process
and through editorial feedback from their presentations. A final international
peer review process was conducted to include input from potential users of the
research, and in particular analytics experts from industry, looking at the impact
of reviewed works.
Many people contribute to an effort such as this, starting with the authors!
We thank all authors for their contributions, and particularly for making the
effort to address two rounds of reviewer comments. Our workshop and conference
reviewers provided the first round of helpful feedback for the presentation of
the papers to their respective conferences. The authors from a selection of the
best papers were then invited to update their contributions for inclusion in this
volume. Each submission was then reviewed by at least another two reviewers
from our international panel of experts in data mining.
A considerable amount of effort goes into reviewing papers, and reviewers
perform an essential task. Reviewers receive no remuneration for all their efforts,
but are happy to provide their time and expertise for the benefit of the whole
community. We owe a considerable debt to them all and thank them for their
enthusiasm and critical efforts.
Bringing this collection together has been quite an effort. We also acknowl-
edge the support of our respective institutions and colleagues who have con-
tributed in many different ways. In particular, Graham would like to thank
Togaware (Data Mining and GNU/Linux consultancy) for their ongoing infras-
tructural support over the years, and the Australian Taxation Office for its
VI
Preface
support of data mining and related local conferences through the participation
of its staff. Simeon acknowledges the support of the University of Technology,
Sydney. The Australian Research Council’s Research Network on Data Min-
ing and Knowledge Discovery, under the leadership of Professor John Roddick,
Flinders University, has also provided support for the associated conferences, in
particular providing financial support to assist student participation in the con-
ferences. Professor Geoffrey Webb, Monash University, has played a supportive
role in the development of data mining in Australia and the AusDM series of
conferences, and continues to contribute extensively to the conference series.
The book is divided into two parts: (i) state-of-art research and (ii) state-
of-art industry applications. The chapters are further grouped around common
sub-themes. We are sure you will find that the book provides an interesting and
broad update on current research and development in data mining.
November 2005
Graham Williams and Simeon Simoff

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