A PSO-based algorithm for mining association rules using a guided exploration strategy.

• A novel Particle swarm optimization based algorithm (PSO-GES), is introduced. • A guided exploration strategy in order to generate high-quality rules, is proposed. • A new fitness funtion for obtain the fitness value in a shorter runtime, is introduced. • PSO-GES uses a summary matrix with a smaller number of rows than the original database. • PSO-GES mines a set of better quality rules than the association rules mining algorithms compared. Association rule mining is one of the most important and active research areas in data mining. In the literature, several association rule miners have been proposed; among them, those based on particle swarm optimization (PSO) have reported the best results. However, these algorithms tend to prematurely fall into local solutions, avoiding a wide exploration that could produce even better results. In this paper, an algorithm based on PSO, called PSO-GES, for mining association rules using a Guided Exploration Strategy is introduced. Our experiments, over real-world transactional databases, show that our proposed algorithm mines better quality association rules than the most recent PSO-based algorithms for mining association rules of the state of the art. [ABSTRACT FROM AUTHOR]