提出约束采样方法CFips，高效挖掘满足条件的区间模式
Frequency-based Constrained Sampling for Interval Patterns
- 将语法约束融入采样流程，分解为区间边界的基本谓词
- 保证采样结果按真实频率分布，且在限定时间内完成任务
- 适合需要快速获取代表性区间模式的研究者
输出空间模式采样是探索大规模模式空间的有效替代方法，可让用户聚焦于依据特定有趣性度量选取的代表性模式。本文针对用户定义语法约束下的区间模式采样问题，提出CFips方法，将约束直接嵌入采样过程。该方法基于多步采样框架，通过将约束分解为区间边界上的基本谓词来支持多种语法约束，并保持精确采样保证。我们形式化证明了CFips在受限模式空间内按模式频率进行比例采样。实验表明，将约束融入采样流程可使原本超时的任务得以完成。
原文摘要 · Abstract (English)
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.
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