说明：  算法首先随机从数据集中选取 K个点作为初始聚类中心，然后计算各个样本到聚类中的距离，把样本归到离它最近的那个聚类中心所在的类。计算新形成的每一个聚类的数据对象的平均值来得到新的聚类中心，如果相邻两次的聚类中心没有任何变化，说明样本调整结束，聚类准则函数已经收敛。本算法的一个特点是在每次迭代中都要考察每个样本的分类是否正确。若不正确，就要调整，在全部样本调整完后，再修改聚类中心，进入下一次迭代。如果在一次迭代算法中，所有的样本被正确分类，则不会有调整，聚类中心也不会有任何变化，这标志着已经收敛，因此算法结束。
(The algorithm first randomly selects K points from the data set as the initial cluster center, then calculates the distance from each sample to the cluster, and returns the sample to the class in which the nearest cluster center is located. The new clustering center is obtained by calculating the average value of the newly formed data objects. If there is no change in the two adjacent cluster centers, the clustering criterion function has been converged. One of the characteristics of this algorithm is to examine the correctness of each sample in each iteration. If it is not correct, we need to adjust it. After all the samples are adjusted, modify the cluster center and enter the next iteration. If all the samples are correctly classified in an iterative algorithm, there will be no adjustment and there will be no change in the cluster center, which signifies the convergence and the end of the algorithm.)