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Pattern Detection and Discovery

ESF Exploratory Workshop, London, UK, September 16-19, 2002.

  • Conference proceedings
  • © 2002

Overview

Editors:
  1. David J. Hand

    1. Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK

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  2. Niall M. Adams

    1. Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK

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    You can also search for this editor in PubMed   Google Scholar

  3. Richard J. Bolton

    1. Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (17 papers)

  1. Front Matter

    Pages I-XII
    Download chapter PDF

  2. General Issues

    1. Pattern Detection and Discovery

      • David J. Hand
      Pages 1-12

    2. Detecting Interesting Instances

      • Katharina Morik
      Pages 13-23

    3. Complex Data: Mining Using Patterns

      • Arno Siebes, Zbyszek Struzik
      Pages 24-35

    4. Determining Hit Rate in Pattern Search

      • Richard J. Bolton, David J. Hand, Niall M. Adams
      Pages 36-48

    5. An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes

      • Paul Cohen, Brent Heeringa, Niall M. Adams
      Pages 49-62

    6. If You Can’t See the Pattern, Is It There?

      • Antony Unwin
      Pages 63-76

  3. Association Rules

    1. Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining

      • Marek Wojciechowski, Maciej Zakrzewicz
      Pages 77-91

    2. Concise Representations of Association Rules

      • Marzena Kryszkiewicz
      Pages 92-109

    3. Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining

      • Baptiste Jeudy, Jean-François Boulicaut
      Pages 110-124

    4. Relational Association Rules: Getting Warmer

      • Bart Goethals, Jan Van den Bussche
      Pages 125-139

  4. Text and Web Mining

    1. Mining Text Data: Special Features and Patterns

      • M. Delgado, M. J. Martín-Bautista, D. Sánchez, M. A. Vila
      Pages 140-153

    2. Modelling and Incorporating Background Knowledge in theWeb Mining Process

      • Myra Spiliopoulou, Carsten Pohle
      Pages 154-169

    3. Modeling Information in Textual Data Combining Labeled and Unlabeled Data

      • Dunja Mladenić
      Pages 170-179

    4. Discovery of Frequent Word Sequences in Text

      • Helena Ahonen-Myka
      Pages 180-189

  5. Applications

    1. Pattern Detection and Discovery: The Case of Music Data Mining

      • Pierre-Yves Rolland, Jean-Gabriel Ganascia
      Pages 190-198

    2. Discovery of Core Episodes from Sequences

      • Frank Höppner
      Pages 199-213

    3. Patterns of Dependencies in Dynamic Multivariate Data

      • Ursula Gather, Roland Fried, Michael Imhoff, Claudia Becker
      Pages 214-226

  6. Back Matter

    Pages 227-227
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Keywords

About this book

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.

Editors and Affiliations

  • Department of Mathematics, Imperial College of Science, Technology and Medicine, London, UK

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

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