# Using Answer Set Programming for pattern mining

arXiv: https://arxiv.org/abs/1409.7777 · HTML: https://mlsanity.com/paper/1409.7777/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2014.

## Built on by (most cited first, 3 of 3)

- [Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming](https://mlsanity.com/paper/2109.08290/) (2021)
- [Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming](https://mlsanity.com/paper/2410.11000/) (2024)
- [Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications](https://mlsanity.com/paper/2303.13191/) (2023)

## Abstract (arXiv)

Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints.