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.