A Novel Approach for Complex Datasets Clustering/Classification

Abstract

This paper introduces a hybrid clustering/classification method for successfully solving the popular clustering/ classifying complex datasets problems. The proposed method for the solution of the clustering /classifying problem, designated as PFV-index method, combines a particle swarm optimization (PSO) algorithm, Fuzzy C-Means (FCM) method, Variable Precision Rough Sets (VPRS) theory and a new cluster validity index function. 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 UCI datasets with those obtained by supervised classification BPNN, decision-tree methods.