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.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.
In the first paper, we discussed regression as well as classification models, and defined 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.
The project is a joint effort of LIACS at Universiteit Leiden and ADA at Universiteit Utrecht. The following people are currently involved:
 Arno Knobbe (UL)
 Ad Feelders (UU)
 Matthijs van Leeuwen (UU)
 Wouter Duivesteijn (UL / UU)
Selected Refereed EMM Publications
2012 

Multilabel LeGo  Enhancing Multilabel Classifiers with Local Patterns. In: Proceedings IDA 2012, 2012. 

Different Slopes for Different Folks  Mining for Exceptional Regression Models with Cook's Distance. In: Proceedings KDD 2012, 2012. 

Diverse Subgroup Set Discovery. In: Data Mining and Knowledge Discovery, special issue ECMLPKDD'12, pp 242208, Springer, 2012. 

2011 

Exploiting False Discoveries  Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. In: Proceedings ICDM 2011, 2011. 

NonRedundant Subgroup Discovery in Large and Complex Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2011 (ECML PKDD'11), 2011. 

2010 

Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), 2010. 

Maximal Exceptions with Minimal Descriptions. In: Data Mining and Knowledge Discovery, special issue ECMLPKDD'10, vol.21(2), pp 259276, Springer, 2010. 

2008 

Exceptional Model Mining. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2008 Part II (ECML PKDD'08), pp 116, 2008. 
Selected Unrefereed EMM Publications
2011 

Exceptional Model Mining, Data Mining: Foundations and Intelligent Paradigms 2, Holmes, D., Jain, L. (eds.), 2011. 