Exceptional Model Mining
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.
Cite
Text
Leman et al. "Exceptional Model Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_1Markdown
[Leman et al. "Exceptional Model Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/leman2008ecmlpkdd-exceptional/) doi:10.1007/978-3-540-87481-2_1BibTeX
@inproceedings{leman2008ecmlpkdd-exceptional,
title = {{Exceptional Model Mining}},
author = {Leman, Dennis and Feelders, Ad and Knobbe, Arno J.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {1-16},
doi = {10.1007/978-3-540-87481-2_1},
url = {https://mlanthology.org/ecmlpkdd/2008/leman2008ecmlpkdd-exceptional/}
}