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Exceptional model mining. (English) Zbl 1231.68206
Holmes, Dawn E. (ed.) et al., Data mining: foundations and intelligent paradigms. Volume 2: Statistical, Bayesian, time series and other theoretical aspects. Berlin: Springer (ISBN 978-3-642-23240-4/hbk; 978-3-642-23241-1/ebook). Intelligent Systems Reference Library 24, 183-198 (2012).
Summary: 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.
For the entire collection see [Zbl 1229.68003].

MSC:
68T05 Learning and adaptive systems in artificial intelligence
68P15 Database theory
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