🤖 AI Summary
This work addresses the computational bottleneck of exhaustive search in interval pattern mining under user-defined syntactic constraints. To overcome this limitation, the authors propose CFips, a novel method that, for the first time, supports diverse syntactic constraints while guaranteeing exact sampling in interval pattern mining. CFips employs a multi-step sampling framework that decomposes complex constraints into elementary predicates over interval boundaries and integrates a frequency-weighted strategy to achieve efficient sampling strictly aligned with the true distribution within the constrained space. Theoretical analysis establishes the correctness of the sampling procedure, and empirical results demonstrate that CFips successfully completes tasks within practical time limits that are intractable for conventional approaches, substantially enhancing both the efficiency and feasibility of constrained pattern mining.
📝 Abstract
Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on representative patterns drawn according to a chosen interestingness measure. In this paper, we address the problem of sampling interval patterns under user-defined syntactic constraints. We introduce CFips, a sampling approach that incorporates constraints directly into the sampling procedure. The approach relies on a multi-step sampling framework and supports several syntactic constraints by decomposing them into elementary predicates on interval bounds while preserving exact sampling guarantees. We formally prove that CFips samples interval patterns proportionally to their frequency within the constrained pattern space. The experimental results show that integrating constraints into the sampling procedure enables to complete mining tasks that would otherwise fail within a given time out.