In this paper, a novel approach for initializing clustering centers of K-Means algorithm is presented.This method is based on the variance of dimension, which is used as keyword to make a full permutation.The results of the full permutation for the primary and secondary sequence of keyword is divided into k subsets to initialize the clustering centers.Four international datesets are used for testing datasets to test the effectiveness of this algorithm.And this algorithm is examined by numerical simulation.Experiments suggest that the initial clustering centers chosen by the optimization method proposed in this paper are very close to the clustering centers of ultimate convergence after clustering iteration.Compared with the traditional K-Means clustering algorithm, this algorithm increase the rationality of algorithm on the initial clustering center selection and improve the accuracy of clustering results, and the clustering results is more stable as well.