K-means Clustering Algorithm with Improved Initial Center

In this paper we present a new clustering method based on K-means that have avoided alternative randomness of initial center. This paper focused on K-means algorithm to the initial value of the dependence of K selected from the aspects of the algorithm is improved. First, the initial clustering number is radicN. Second, through the application of the sub-merger strategy the categories were combined.The algorithm does not require the user is given in advance the number of cluster. Experiments on synthetic datasets are presented to have shown significant improvements in clustering accuracy in comparison with the random K-means.