Year: 2015
Type: preprint
Abstract: Working with exhaustive search on large dataset is infeasible for several reasons. Recently, developed techniques that made pattern set mining feasible by a general solver with long execution time that supports heuristic search and are limited to small datasets only. In this paper, we investigate an approach which aims to find diverse set of patterns using genetic algorithm to mine diverse frequent patterns. We propose a fast heuristic search algorithm that outperforms state-of-the-art methods on a standard set of benchmarks and capable to produce satisfactory results within a short period of time. Our proposed algorithm uses a relative encoding scheme for the patterns and an effective twin removal technique to ensure diversity throughout the search.
Source: arXiv (Cornell University)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2951485135