Data Clustering with Particle Swarms

This paper presents a new proposal for data clustering based on the particle swarm optimization (PSO) algorithm. The human tendency of adapting its behavior due to the influence of the environment minimizing the differences in opinions and ideas through time and taking into account the past experiences characterizes an emergent social behavior. In the PSO algorithm, each individual in the population searches for a solution taking into account the best individual in a certain neighborhood and its own past best solution as well. In the present work, the PSO algorithm was adapted to position prototypes (particles) in regions of the space that represent natural clusters of the input data set. The proposed method, named particle swarm clustering (PSC) algorithm, was applied in an unsupervised fashion to a number of benchmark classification problems and to one bioinformatics dataset in order to evaluate its performance.