Visual Data Mining Theory, Techniques and Tools for Visual Analytics
The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visu
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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 Alfred Kobsa University of California, Irvine, CA, 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 University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany
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Simeon J. Simoff Michael H. Böhlen Arturas Mazeika (Eds.)
Visual Data Mining Theory, Techniques and Tools for Visual Analytics
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Volume Editors Simeon J. Simoff University of Western Sydney School of Computing and Mathematics NSW 1797, Australia E-mail: [email protected] Michael H. Böhlen Arturas Mazeika Free University of Bozen-Bolzano Faculty of Computer Science Dominikanerplatz 3, 39100 Bozen-Bolzano, Italy E-mail: {boehlen,arturas}@inf.unibz.it
Library of Congress Control Number: 2008931578 CR Subject Classification (1998): H.2.8, I.3, H.5 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. Internet/Web and HCI ISSN ISBN-10 ISBN-13
0302-9743 3-540-71079-5 Springer Berlin Heidelberg New York 978-3-540-71079-0 Springer Berlin Heidelberg New York
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Foreword Visual Data Mining—Opening the Black Box
Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. The nature of what makes a rule interesting to a user has been discussed widely1 but most agree that it is a subjective quality based on the practical usefulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can influence the behavior of the system. There have been some impressive research activities
