No 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.

-The Internet can be seen as a major repository of resources and information. The growing demand for information, along with the large amount of data available, has been stimulating the research of methods for text mining. This work aims at using feature selection and text clustering techniques based on a Particle Swarm Clustering (PSC) algorithm and on an Artificial Neural Network modeled as a competitive and constructive Antibody Network, called RABNET (Real-valued Antibody Network), to show that both techniques present relevant results when applied to text clustering problems.