Stochastic Local Search for Pattern Set Mining

TL;DR

This paper explores the use of stochastic local search algorithms to efficiently solve pattern set mining problems, particularly in concept learning, where exhaustive methods are impractical due to large search spaces.

Contribution

It introduces stochastic local search techniques for pattern set mining, demonstrating their potential in handling large search spaces in concept learning tasks.

Findings

Local search algorithms outperform exhaustive methods in large search spaces

Results show promising potential for stochastic methods in pattern set mining

Benchmark tests indicate effective solution quality and efficiency

Abstract

Local search methods can quickly find good quality solutions in cases where systematic search methods might take a large amount of time. Moreover, in the context of pattern set mining, exhaustive search methods are not applicable due to the large search space they have to explore. In this paper, we propose the application of stochastic local search to solve the pattern set mining. Specifically, to the task of concept learning. We applied a number of local search algorithms on a standard benchmark instances for pattern set mining and the results show the potentials for further exploration.