Exceptional Model Mining

Authors:
Dennis Leman;Ad Feelders;Arno Knobbe
Affiliations:
Utrecht University, TB Utrecht, the Netherlands NL-3508;Utrecht University, TB Utrecht, the Netherlands NL-3508;Utrecht University, TB Utrecht, the Netherlands NL-3508 and Kiminkii, Houten, the Netherlands NL-3990
Venue:
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Year:
2008

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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 introduce 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.