PSO-FSPMiner: A Metaheuristic Approach for Mining a Representative Subset of Frequent Similar Patterns

In recent years, algorithms employing similarity functions beyond equality to unveil hidden knowledge have surged in popularity. Nonetheless, a notable challenge accompanying these algorithms is the proliferation of numerous frequent similar patterns, leading to heightened computational overhead and complicating analysis for humans. This paper proposes a metaheuristic approach based on Particle Swarm Optimization (PSO-FSPMiner) that extracts a representative subset of patterns to tackle this issue. Our experiments on real-world datasets demonstrate that the subset of frequent similar patterns mined by PSO-FSPMiner captures approximately 86.4% of the dataset’s knowledge, with a substantial reduction in frequent similar patterns of around 85.9%.