Flexible constrained sampling with guarantees for pattern mining

TL;DR

This paper introduces Flexics, a flexible and accurate pattern sampling algorithm that supports various quality measures and constraints, enabling efficient pattern exploration with guarantees on sampling accuracy.

Contribution

Flexics is the first pattern sampler supporting broad constraints and quality measures with strong guarantees, leveraging SAT-based solutions and pattern mining as constraint satisfaction.

Findings

Flexics provides accurate sampling with strong guarantees.

Flexics efficiently handles diverse constraints and quality measures.

Empirical results show Flexics outperforms existing methods in accuracy and speed.

Abstract

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.