Review of Heuristic Algorithms for Frequent Itemsets Mining Problem

Abstract

Frequent Itemsets Mining (FIM), which consists of extracting frequent patterns from a transactional database, is considered one of the most successful techniques in data mining. Generally, the FIM problem can be solved by either the exact or metaheuristic-based methods. Exact methods, such as the Apriori algorithm, are highly effective for dealing with small to medium datasets. However, these methods need more temporal complexity when dealing with large datasets. Metaheuristic-based methods are becoming more rapid, but the majority still need to be more precise. Several studies were carried out to address these issues and improve metaheuristics-based approaches by combining the Apriori algorithm with several metaheuristics algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result of this combination gave birth to two approaches: GA-Apriori and PSO-Apriori. Consequently, after performing several studies on different database instances, the results revealed that the two approaches outperformed the Apriori algorithm in terms of runtime. PSO-Apriori also beats GA-Apriori in terms of both runtime and solution efficiency.