ROCsearch: an ROC-guided search strategy for subgroup discovery

Subgroup Discovery (SD) aims to find coherent, easy-to-interpret subsets of the dataset at hand, where something exceptional is going on. Since the resulting subgroups are defined in terms of conditions on attributes of the dataset, this data mining task is ideally suited to be used by non-expert analysts. The typical SD approach uses a heuristic beam search, involving parameters that strongly influence the outcome. Unfortunately, these parameters are often hard to set properly for someone who is not a data mining expert; correct settings depend on properties of the dataset, and on the resulting search landscape. To remove this potential obstacle for casual SD users, we introduce ROCsearch [1], a new ROC-based beam search variant for Subgroup Discovery. On each search level of the beam search, ROCsearch analyzes the interme-diate results in ROC space to automatically determine a sensible search width for the next search level. Thus, beam search parameter setting is taken out of the do-main expert’s hands, lowering the threshold for using Subgroup Discovery. Also, ROCsearch automatically adapts its search behavior to the properties and re-sulting search landscape of the dataset at hand. Aside from these advantages, we also show that ROCsearch is an order of magnitude more efficient than traditional beam search, while its results are equivalent and on large datasets even better than traditional beam search results

Anytime Discovery of a Diverse Set of Patterns with Monte Carlo Tree Search

International audienceThe discovery of patterns that accurately discriminate one class label from another remains a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that enables to elicit such interesting patterns from labeled data. A question remains fairly open: How to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is infeasible? Existing approaches make use of beam-search, sampling, and genetic algorithms for discovering a pattern set that is non-redundant and of high quality w.r.t. a pattern quality measure. We argue that such approaches produce pattern sets that lack of diversity: Only few patterns of high quality, and different enough, are discovered. Our main contribution is then to formally define pattern mining as a game and to solve it with Monte Carlo tree search (MCTS). It can be seen as an exhaustive search guided by random simulations which can be stopped early (limited budget) by virtue of its best-first search property. We show through a comprehensive set of experiments how MCTS enables the anytime discovery of a diverse pattern set of high quality. It out-performs other approaches when dealing with a large pattern search space and for different quality measures. Thanks to its genericity, our MCTS approach can be used for SD but also for many other pattern mining tasks