Effective method for cluster centers'' initialization in K-means clustering
一种有效的K-means聚类中心初始化方法*

Initializing cluster centers randomly, traditional K-means algorithm leads to great fluctuations in the clustering results. The existing max-min distance algorithm, indeed, has rather dense cluster centers, which may easily bring about clustering conflicts. To solve these problems, this paper regarded the existing max-min distance algorithm as the thinking foundation and proposed the maximum distances product algorithm. Based on the theory of density-based clustering, the maximum distances product algorithm selected each point which had maximum product of distances between itself and all other initialized clustering centers. Theory analysis and experimental results show that compared with traditional K-means algorithm and max-min distance algorithm, the maximum distances product algorithm can result in faster convergence speed, higher accuracy, greater stability.