Efficient Clustering of Dataset Based on Differential Evolution

Abstract

A novel approach to combining feature selection and clustering is presented. It uses selection of weighted Principal Components for features selection and automatic clustering based on Improved DE for clustering in order to reduce the complexity of high dimensional datasets and speed up the DE clustering process. We report significant improvements in total runtime. Moreover, the clustering accuracy of the dimensionality reduction DE clustering algorithm is comparable to the one that uses full dimensional datasets. The efficiency of this approach has been demonstrated with some real life datasets.