Intelligent Data Engineering And Automated Learning - Ideal 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005. Proceedings

Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos New York University, NY, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany 3578 Marcus Gallagher James Hogan Frederic Maire (Eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2005 6th International Conference Brisbane, Australia, July 6-8, 2005 Proceedings 13 Volume Editors Marcus Gallagher University of Queensland School of Information Technology and Electrical Engineering Brisbane Qld 4072, Australia E-mail: [email protected] James Hogan Frederic Maire Queensland University of Technology School of Software Engineering and Data Communications GPO Box 2434, Brisbane Qld 4001, Australia E-mail: {j.hogan,f.maire}@qut.edu.au Library of Congress Control Number: 2005928541 CR Subject Classification (1998): H.2.8, F.2.2, I.2, F.4, K.4.4, H.3, H.4 ISSN ISBN-10 ISBN-13 0302-9743 3-540-26972-X Springer Berlin Heidelberg New York 978-3-540-26972-4 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 springeronline.com © Springer-Verlag Berlin Heidelberg 2005 Printed in Germany Typesetting: Camera-ready by author, data conversion by Olgun Computergrafik Printed on acid-free paper SPIN: 11508069 06/3142 543210 Preface The ongoing success of the Intelligent Data Engineering and Automated Learning (IDEAL) conference series reflects the continuing need for intelligent approaches to understanding relationships in the massive data sets which confront the modern researcher. From its origins in Hong Kong in 1998, this focus upon the nature of the data has been the unifying theme of the conference, allowing it to become a key forum for researchers to present novel approaches to data engineering and learning, and to provide a partic