Pattern Detection and Discovery

1. Front Matter

   Pages I-XII

   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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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.

• 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
• algorithm analysis and problem complexity

• 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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