New initialization method for cluster center
聚类中心初始化的新方法

The k-means clustering algorithm is prone to be trapped into local optima by inappropriate initial cluster centers. For this reason, the existing initialization methods for the cluster center have not been widely accepted. We assume that there is at least one dense subset of data in a cluster; and the dense subsets between different clusters are more distant than those in the same cluster. A minimum spanning tree is built for the given data set. The dense subsets can be found through the search from root trees, and their densities are obtained by the estimation technique for data density. The initial cluster centers are picked out from the dense subsets that are dense enough and distant enough from each other. The comparisons between the proposed method and current methods show that the performance of the proposed method is promising.