Abstract:MonteCloPi is an anytime subgroup discovery algorithm based on Monte Carlo tree search (MCTS). It aims to build an asymmetric best-first search tree to discover a diverse pattern set with high quality by MCTS policies, while it is limited to a binary target. To this end, this study combines the characteristics of the numerical target to extend the MonteCloPi algorithm to the numerical target. The study selects the appropriate C value for the upper confidence bound (UCB) formula, adjusts the expansion weight of each sample dynamically as well as prunes the search tree, and uses the adaptive top-k-mean-update policy. Finally, the experimental results on the UCI datasets and the National Health and Nutrition Examination Survey (NHANES) audiometry datasets show that the proposed algorithm outperforms other algorithms in terms of discovering diverse pattern sets with high quality and the interpretability of the best subgroup.