k-means是一种非监督 （从下图 0 当中我们可以看到训练数据并没有标签标注类别）的聚类算法：
K-Means clustering intends to partition n objects into k clusters in which each object belongs to the cluster with the nearest mean. This method produces exactly k different clusters of greatest possible distinction. The best number of clusters k leading to the greatest separation (distance) is not known as a priori and must be computed from the data. The objective of K-Means clustering is to minimize total intra-cluster variance, or, the squared error function:
0.initial
1.select centroids randomly
2.assign points
3.update centroids
4.reassign points
5.update centroids
6.reassign points
7.iteration
reference：
https://www.naftaliharris.com/blog/visualizing-k-means-clustering/
https://www.saedsayad.com/clustering_kmeans.htm
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