Estudo de algoritmos híbridos para clusterização de dados usando pso

Abstract

The amount of information available and collected nowadays is much greater than the human capacity of processing it. This problem motivates the development of computational methods capable of extracting useful knowledge such data efficiently. The Clustering problem consists on finding groups on a certain data set and is one of the main tasks on knowledge discovery from data bases. The Particle Swarm Optimization is a rather new method based on social behavior that has been successfully applied to many kinds of problems. Recently, some approaches were proposed modifying the PSO to the Clustering problem. These methods are recent and still can be part of research objects. This work aims to studying of hybrid algorithms using Particle Swarm Optimization to solve the problem of clustering data.