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The discovery of patterns that accurately discriminate one class label fromanother remains a challenging data mining task. Subgroup discovery (SD) is oneof the frameworks that enables to elicit such interesting hypotheses fromlabeled data. A question remains fairly open: How to select an accurateheuristic search technique when exhaustive enumeration of the pattern space isinfeasible? Existing approaches make use of beam-search, sampling and geneticalgorithms for discovering a pattern set that is non-redundant and of highquality w.r.t. a pattern quality measure. We argue that such approaches producepattern sets that lack of diversity: Only few patterns of high quality, anddifferent enough, are discovered. Our main contribution is then to formallydefine 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 simulationswhich can be stopped early (limited budget) by virtue of its best-first searchproperty. We show through a comprehensive set of experiments how MCTS enablesthe anytime discovery of a diverse pattern set of high quality. It outperformsother approaches when dealing with a large pattern search space and fordifferent quality measures. Thanks to its genericity, our MCTS settings can beused for SD but also for many other pattern mining tasks.

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