A hybrid particle swarm optimization approach for clustering and classification of datasets

Abstract

This paper introduces a new hybrid cluster validity method based on particle swarm optimization, for successfully solving one of the most popular clustering/classifying complex datasets problems. The proposed method for the solution of the clustering/classifying problem, designated as PSORS index method, combines a particle swarm optimization (PSO) algorithm, Rough Set (RS) theory and a modified form of the Huang index function. In contrast to the Huang index method which simply assigns a constant number of clusters to each attribute, this method could cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is investigated by comparing the classification results obtained for a real-world dataset with those obtained by pseudo-supervised classification BPNN, decision-tree and Huang index methods. There is good evidence to show that the proposed PSORS index method not only has a superior clustering accomplishment than the considered methods, but also achieves better classification accuracy.