An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns

TL;DR

This paper introduces a fast genetic algorithm for mining diverse frequent patterns in large datasets, outperforming existing methods in efficiency and diversity.

Contribution

It presents a novel heuristic search algorithm with a unique encoding scheme and twin removal technique, enabling efficient discovery of diverse patterns.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Produces diverse pattern sets within short execution times

Effective in large-scale pattern set mining

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.