How to determine the number of clusters is always a difficult problem in data cluster analysis.Therefore

a novel dynamic genetic clustering algorithm (DGCA) is proposed in this paper.The DGCA adopts a maximum attribute range partition method to overcome the sensitiveness to initial values of cluster centers for clustering algorithms.Furthermore

the two-stage dynamic selection and mutation operations are used in the DGCA to make selection probability and mutation probability vary with the consistency of the number of clusters in the population.Firstly the parallel search in different numbers of clusters is carried out.Then the optimal search for the best cluster centers is conducted.Numerical experiments on seven data sets show that the proposed DGCA can realize the global search for the best partition and find the optimal values for both the number of clusters and the cluster centers.