k-Means的陷阱

k-Means的5個step（how did it do that?）：

step 1 : choose the number K of clusters

step 2 : select at random k points , the centroids (not necessarily from your dataset )

step 3 : Assign each data point to the closest centroid (that from k clusters)

step 4 : compute and place the new centroid of each cluster

step 5 : reassign each data point to the new closest centroid.

if any reassignment took place(Your Model is Ready), go to step 4 ,otherwise go to FIN

k-Means random Initialization trap：choosing the right number of clusters

the solution is k-means ++ 

the Elbow Method:

圖中顯示：k-Means中選擇3個cluster時，WCSS距離值改變的趨勢基本不明顯，於是選擇K=3

參考博文：

https://blog.csdn.net/u011730199/article/details/78108263 

https://www.cnblogs.com/sharon123/p/6828853.html    

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