A Differential Evolution-Based Clustering for Probability Density Functions

Abstract

Clustering for probability density functions (CDFs) has recently emerged as a new interest technique in statistical pattern recognition because of its potential in various practical issues. For solving the CDF problems, evolutionary techniques which are successfully applied in clustering for discrete elements have not been studied much in CDF. Therefore, this paper presents for the first time an application of the differential evolution (DE) algorithm for clustering of probability density functions (pdfs) in which the clustering problem is transformed into an optimization problem. In this optimization problem, the objective function is to minimize the internal validity measure-SF index, and the design variable is the name of the cluster in which pdfs are assigned to. To solve this optimization problem, a DE-based CDF is proposed. The efficiency and feasibility of the proposed approach are demonstrated through four numerical examples including analytical and real-life problems with gradually increasing the complexity of the problem. The obtained results mostly outperform several results of compared algorithms in the literature.