Generic Pattern Trees for Exhaustive Exceptional Model Mining
Abstract
Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.
Cite
Text
Lemmerich et al. "Generic Pattern Trees for Exhaustive Exceptional Model Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_18Markdown
[Lemmerich et al. "Generic Pattern Trees for Exhaustive Exceptional Model Mining." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/lemmerich2012ecmlpkdd-generic/) doi:10.1007/978-3-642-33486-3_18BibTeX
@inproceedings{lemmerich2012ecmlpkdd-generic,
title = {{Generic Pattern Trees for Exhaustive Exceptional Model Mining}},
author = {Lemmerich, Florian and Becker, Martin and Atzmueller, Martin},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {277-292},
doi = {10.1007/978-3-642-33486-3_18},
url = {https://mlanthology.org/ecmlpkdd/2012/lemmerich2012ecmlpkdd-generic/}
}