Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks

TL;DR

This paper explores using Answer Set Programming (ASP) for sequential pattern mining, comparing different encodings and representations to evaluate efficiency and memory use, and benchmarking against constraint programming.

Contribution

It introduces ASP encodings for sequential pattern mining with various representations and patterns, and compares their performance to other declarative approaches.

Findings

Fill-gaps encoding uses less memory on real problems.

ASP encodings perform comparably to constraint programming.

Fill-gaps strategy is more efficient than skip-gaps in practice.

Abstract

This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance.