Generic pattern trees for exhaustive exceptional model mining

Authors:
Florian Lemmerich;Martin Becker;Martin Atzmueller
Affiliations:
Artificial Intelligence and Applied Computer Science Group, University of Würzburg, Würzburg, Germany;Artificial Intelligence and Applied Computer Science Group, University of Würzburg, Würzburg, Germany;Knowledge & Data Engineering Group, University of Kassel, Kassel, Germany
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Year:
2012

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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.