In -means clustering

we are given a set of n data points in -dimensional space and an integer the problem is to determine a set of points in

called centers

so as to minimize the mean squared distance from each data point to its nearest center. The initial centers of direct -means algorithm are chosen randomly

different initial centers will lead to different results. In this paper

in view of the deficiency of direct -means algorithm

we propose a novel method about initial centers based on sorting and partition and apply it to real data as well as simulated data

which show that this is a simple and efficient method to improve the clustering accuracy and efficiency.