A scalable, distributed framework for significant subgroup discovery

Abstract

Subgroup discovery is a supervised data mining technique having many applications in medical domains, market basket analysis, and social media analysis. It helps in mining subgroups (or patterns) with a high association to a target property, measured using a quality function. However, the process is computationally intensive as it is necessary to go through the search space of all subgroups to find the top-k interesting ones w.r.t. the quality function. Further, as we verify many associations, it is quite possible that a certain level of association might be achieved by chance. To address this issue, the state-of-the-art TopKWY algorithm employs permutation testing to control false discoveries. Still, testing multiple subgroups against thousands of permuted target labels further increases computational complexity. Additionally, TopKWY is limited to a specific quality function and lacks a parallel/distributed implementation to handle scalability challenges. In this paper, we propose a parallel and distributed framework for subgroup discovery named ParaDiS that extends permutation testing to a broader class of quality functions. ParaDiS scales to large datasets while effectively controlling the false discovery rate. It features different optimizations to reduce communication/computation overheads and a distributed best-first search strategy to improve pruning across different workers. We compare its performance for several real-world datasets and achieve an order of magnitude reduction in the execution time compared to the sequential approach.