The behavior of particles in the Particle Swarm Clustering algorithm

Abstract

The Particle Swarm Clustering (PSC) algorithm uses collective intelligence to solve clustering problems. It simulates the interaction of individuals, which use their own experience (cognitive term), social experience (social term) and interaction with the environment (self-organizing term) to cluster objects in different groups. In this work a study of the behavior of particles and an analysis of the PSC convergence were performed considering each term that composes the particles' adaptation equation. The objective was to evaluate the relevance of each of these terms within the context of clustering data.