document: Sampling-based sequential subgroup mining 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 .
doc_bio_tags: O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O