K均值有效执行++多元数据的聚类算法。它已经表明，该算法具有的总群集内距离的期望值是日志（K）的竞争力的上限。此外，K -均值++通常远高于香草收敛K均值少。-An efficient implementation of the k-means++ algorithm for clustering multivariate data. It has been shown that this algorithm has an upper bound for the expected value of the total intra-cluster distance which is log(k) competitive. Additionally, k-means++ usually converges in far fewer than vanilla k-means.