## Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected PapersIntroduction 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. |

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### Contents

Pushing Constraints to Detect Local Patterns | 1 |

Evaluation Issues in Rule Learning Algorithms | 20 |

Pattern Discovery Tools for Detecting Cheating in Student Coursework | 39 |

Are There Substantive Differences? | 53 |

Theory and Practice of ConstraintBased Relational Subgroup Discovery | 71 |

Visualizing Very Large Graphs Using Clustering Neighborhoods | 89 |

Features for Learning Local Patterns in TimeStamped Data | 98 |

An Application to Gene Expression Data Analysis | 115 |

Local Pattern Discovery in ArrayCGH Data | 135 |

Learning with Local Models | 153 |

KnowledgeBased Sampling for Subgroup Discovery | 171 |

Temporal Evolution and Local Patterns | 190 |

Undirected Exception Rule Discovery as Local Pattern Detection | 207 |

From Local to Global Analysis of Music Time Series | 217 |

232 | |

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analysis applied approach association rules attributes background model BACs boolean Boulicaut classifier clustering algorithm combination computed context covered data density Data Mining data objects data set database David Hand decision trees defined dendrograms denote deviation Discovery and Data distribution domain estimation evaluation exception rule FGFR3 Figure Flach frequency frequent itemsets frequent pattern function Fürnkranz gene expression gene expression data given global model graph heuristic induction Inductive Logic Programming interesting interestingness International Conference iteration Knowledge Discovery Lavrac learner Machine Learning measures method metrics Morik observations parameters Pattern Detection pattern discovery pattern mining performance precision prediction prior knowledge problem Proceedings properties pruning regions representation rule discovery rule induction rule pairs rulesets sampling SDRI Section sequences similarity scores Springer statistical structure subgroup discovery subset Support Vector Machines TCat model techniques temporal TF/IDF threshold tree tumour unsupervised learning values vector WRAcc