Stochastic Local Search for Pattern Set Mining
This work addresses the computational challenge of pattern set mining for researchers and practitioners, but it is incremental as it applies existing local search algorithms to a known problem.
The authors tackled the problem of pattern set mining, particularly concept learning, by applying stochastic local search methods to efficiently find solutions in large search spaces where exhaustive methods fail, and results on benchmark instances indicate potential for further exploration.
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.