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Pattern Detection and Discovery

ESF Exploratory Workshop London, UK, September 16–19, 2002 Proceedings

  • David J. Hand
  • Niall M. Adams
  • Richard J. Bolton

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2447)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2447)

Table of contents

  1. Front Matter
    Pages I-XII
  2. General Issues

    1. David J. Hand
      Pages 1-12
    2. Katharina Morik
      Pages 13-23
    3. Arno Siebes, Zbyszek Struzik
      Pages 24-35
    4. Richard J. Bolton, David J. Hand, Niall M. Adams
      Pages 36-48
    5. Paul Cohen, Brent Heeringa, Niall M. Adams
      Pages 49-62
  3. Association Rules

    1. Marek Wojciechowski, Maciej Zakrzewicz
      Pages 77-91
    2. Marzena Kryszkiewicz
      Pages 92-109
    3. Bart Goethals, Jan Van den Bussche
      Pages 125-139
  4. Text and Web Mining

    1. M. Delgado, M. J. Martín-Bautista, D. Sánchez, M. A. Vila
      Pages 140-153
    2. Myra Spiliopoulou, Carsten Pohle
      Pages 154-169
    3. Helena Ahonen-Myka
      Pages 180-189
  5. Applications

    1. Pierre-Yves Rolland, Jean-Gabriel Ganascia
      Pages 190-198
    2. Frank Höppner
      Pages 199-213
    3. Ursula Gather, Roland Fried, Michael Imhoff, Claudia Becker
      Pages 214-226
  6. Back Matter
    Pages 227-227

About these proceedings

Introduction

The collation of large electronic databases of scienti?c and commercial infor- tion has led to a dramatic growth of interest in methods for discovering struc- res in such databases. These methods often go under the general name of data mining. One important subdiscipline within data mining is concerned with the identi?cation and detection of anomalous, interesting, unusual, or valuable - cords or groups of records, which we call patterns. Familiar examples are the detection of fraud in credit-card transactions, of particular coincident purchases in supermarket transactions, of important nucleotide sequences in gene sequence analysis, and of characteristic traces in EEG records. Tools for the detection of such patterns have been developed within the data mining community, but also within other research communities, typically without an awareness that the - sic problem was common to many disciplines. This is not unreasonable: each of these disciplines has a large literature of its own, and a literature which is growing rapidly. Keeping up with any one of these is di?cult enough, let alone keeping up with others as well, which may in any case be couched in an - familiar technical language. But, of course, this means that opportunities are being lost, discoveries relating to the common problem made in one area are not transferred to the other area, and breakthroughs and problem solutions are being rediscovered, or not discovered for a long time, meaning that e?ort is being wasted and opportunities may be lost.

Keywords

Algorithmic Learning Association Ruel Mining Data Analysis Pattern Detection Pattern Discovery Pattern Mining Pattern Searches Text Mining Usage Pattern Detection Web Mining Web Usage Mining algorithms data mining filtering modeling

Editors and affiliations

  • David J. Hand
    • 1
  • Niall M. Adams
    • 1
  • Richard J. Bolton
    • 1
  1. 1.Department of MathematicsImperial College of Science, Technology and MedicineLondonUK

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-45728-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-44148-9
  • Online ISBN 978-3-540-45728-2
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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