Bump hunting in high-dimensional data
Statistics and Computing
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
On subgroup discovery in numerical domains
Data Mining and Knowledge Discovery
Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Exceptional Model Mining (EMM) is an exploratory data analysis technique that can be regarded as a generalization of subgroup discovery. In EMM we look for subgroups of the data for which a model fitted to the subgroup differs substantially from the same model fitted to the entire dataset. In this paper we develop methods to mine for exceptional regression models. We propose a measure for the exceptionality of regression models (Cook's distance), and explore the possibilities to avoid having to fit the regression model to each candidate subgroup. The algorithm is evaluated on a number of real life datasets. These datasets are also used to illustrate the results of the algorithm. We find interesting subgroups with deviating models on datasets from several different domains. We also show that under certain circumstances one can forego fitting regression models on up to 40% of the subgroups, and these 40% are the relatively expensive regression models to compute.