Exceptional Model Mining
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.
KeywordsQuality Measure Sales Price Decision Table Output Attribute Hellinger Distance
Unable to display preview. Download preview PDF.
- 1.Affymetrix (1992), http://www.affymetrix.com/index.affx
- 2.Heckerman, D., Geiger, D., Chickering, D.: Learning Bayesian Networks: The combination of knowledge and statistical data. Machine Learning 20, 179–243 (1995)Google Scholar
- 6.Knobbe, A.: Safarii multi-relational data mining environment (2006), http://www.kiminkii.com/safarii.html
- 8.Kohavi, R.: The Power of Decision Tables. In: Proceedings ECML1995, London (1995)Google Scholar
- 10.van de Koppel, E., et al.: Knowledge Discovery in Neuroblastoma-related Biological Data. In: Data Mining in Functional Genomics and Proteomics workshop at PKDD 2007, Warsaw, Poland (2007)Google Scholar
- 11.Moore, D., McCabe, G.: Introduction to the Practice of Statistics, New York (1993)Google Scholar
- 12.Neter, J., Kutner, M., Nachtsheim, C.J., Wasserman, W.: Applied Linear Statistical Models. WCB McGraw-Hill, New York (1996)Google Scholar
- 14.Xu, Y., Fern, A.: Learning Linear Ranking Functions for Beam Search. In: Proceedings ICML 2007 (2007)Google Scholar
- 15.Niculescu-Mizil, A., Caruana, R.: Inductive Transfer for Bayesian Network Structure Learning. In: Proceedings of the 11th International Conference on AI and Statitics, AISTATS 2007 (2007)Google Scholar