Clustering based on differential evolution algorithm with weighted validity function

A Differential Evolution Clustering algorithm with weighted validity function is presented in this paper, five validity functions are selected to form the fitness function with weights, and in selection of Differential Evolution, individuals not being selected are put into secondary population. During evolution, individuals in secondary population replace those in main population if their fitness values are less than those in main population. We have carried out experiments on 3 datasets from UCI machine learning repository and compared validity results to those from K-Means and classical Differential Evolution, experimental results show that our approach can improve clustering performance.

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