Local Pattern Detection

1. Front Matter

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2. Pushing Constraints to Detect Local Patterns

   • Francesco Bonchi, Fosca Giannotti

   Pages 1-19

3. From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms

   • Johannes Fürnkranz

   Pages 20-38

4. Pattern Discovery Tools for Detecting Cheating in Student Coursework

   • David J. Hand, Niall M. Adams, Nick A. Heard

   Pages 39-52

5. Local Pattern Detection and Clustering

   • Frank Höppner

   Pages 53-70

6. Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery

   • Nada Lavrač, Filip Železný, Sašo Džeroski

   Pages 71-88

7. Visualizing Very Large Graphs Using Clustering Neighborhoods

   • Dunja Mladenic, Marko Grobelnik

   Pages 89-97

8. Features for Learning Local Patterns in Time-Stamped Data

   • Katharina Morik, Hanna Köpcke

   Pages 98-114

9. Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis

   • Ruggero G. Pensa, Jean-François Boulicaut

   Pages 115-134

10. Local Pattern Discovery in Array-CGH Data

    • Céline Rouveirol, Francois Radvanyi

    Pages 135-152

11. Learning with Local Models

    • Stefan Rüping

    Pages 153-170

12. Knowledge-Based Sampling for Subgroup Discovery

    • Martin Scholz

    Pages 171-189

13. Temporal Evolution and Local Patterns

    • Myra Spiliopoulou, Steffan Baron

    Pages 190-206

14. Undirected Exception Rule Discovery as Local Pattern Detection

    • Einoshin Suzuki

    Pages 207-216

15. From Local to Global Analysis of Music Time Series

    • Claus Weihs, Uwe Ligges

    Pages 217-231

16. Back Matter

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Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.

• algorithmic learning
• algorithms
• calculus
• data analysis
• data mining
• learning
• pattern detection
• pattern discovery
• pattern mining
• pattern searches
• text mining
• usage pattern detection
• algorithm analysis and problem complexity

• Computer Science VIII, artificial Intelligence Unit, Technische Universität Dortmund, Dortmund, Germany

  Katharina Morik

• INSA-Lyon, LIRIS CNRS UMR5205, Villeurbanne, France

  Jean-François Boulicaut

• Department of Computer Science, Universiteit Utrecht,  

  Arno Siebes

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