Sampling-based sequential subgroup mining

Abstract

Subgroup discovery is a learning task that aims at finding interesting rules from classified examples. The search is guided by a utility function, trading off the coverage of rules against their statistical unusualness. One shortcoming of existing approaches is that they do not incorporate prior knowledge. To this end a novel generic sampling strategy is proposed. It allows to turn pattern mining into an iterative process. In each iteration the focus of subgroup discovery lies on those patterns that are unexpected with respect to prior knowledge and previously discovered patterns. The result of this technique is a small diverse set of understandable rules that characterise a specified property of interest. As another contribution this article derives a simple connection between subgroup discovery and classifier induction. For a popular utility function this connection allows to apply any standard rule induction algorithm to the task of subgroup discovery after a step of stratified resampling. The proposed techniques are empirically compared to state of the art subgroup discovery algorithms.

DOI: 10.1145/1081870.1081902

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Cite this paper

@inproceedings{Scholz2005SamplingbasedSS, title={Sampling-based sequential subgroup mining}, author={Martin Scholz}, booktitle={KDD}, year={2005} }