- 期刊
A Hybrid Clustering/Classification Method for Complex Datasets Based on Variable Precision Rough Sets and Validity Index Function
並列摘要
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. There is good evidence to show that the proposed PFV-index method not only has a superior clustering accomplishment than the considered methods, but also achieves better classification accuracy.