具有模式挖掘保证的灵活约束抽样
Flexible constrained sampling with guarantees for pattern mining
摘要：
模式采样已经被提出作为臭名昭著的模式爆炸的潜在解决方案。不是枚举满足约束的所有模式，而是与给定的质量度量成比例地对单个模式进行采样。已经提出了几种采样算法，但是它们中的每一种都有其局限性，当涉及到1）在可以使用的质量测量和约束方面的灵活性，和/或2）关于采样精度的保证时。因此，我们提出Flexics，第一个灵活的模式采样器，支持广泛的质量措施和约束，同时提供强有力的保证采样精度。为了实现这一目标，我们利用模式挖掘的角度作为一个约束满足问题，并建立在SAT采样解决方案的最新进展，以及现有的模式挖掘算法。此外，所提出的算法适用于各种模式语言，这使我们能够引入和解决采样模式集的新任务。我们介绍和经验评估两种变体的Flexics：1）一个通用的变体，解决了众所周知的项集采样任务和新颖的模式集采样任务，以及在这些任务中的广泛的表达约束，和2）一个专门的变体，利用现有的频繁项集技术，以实现实质性的加速。实验表明，Flexics是准确和高效的，使其成为一个有用的工具，基于模式的数据探索。
英文摘要：
Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
