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Exceptional Model Mining

  • Arno Knobbe
  • Ad Feelders
  • Dennis Leman
Part of the Intelligent Systems Reference Library book series (ISRL, volume 24)

Abstract

In most databases, it is possible to identify small partitions of the data where the observed distribution is notably different from that of the database as a whole. In classical subgroup discovery, one considers the distribution of a single nominal attribute, and exceptional subgroups show a surprising increase in the occurrence of one of its values. In this paper, we describe Exceptional Model Mining (EMM), a framework that allows for more complicated target concepts. Rather than finding subgroups based on the distribution of a single target attribute, EMM finds subgroups where a model fitted to that subgroup is somehow exceptional. We discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling.

Keywords

Quality Measure Sales Price Decision Table Output Attribute Hellinger Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arno Knobbe
    • 1
  • Ad Feelders
    • 2
  • Dennis Leman
    • 2
  1. 1.LIACS, Leiden UniversityLeidenThe Netherlands
  2. 2.Utrecht UniversityUtrechtThe Netherlands

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