k-Means的陷阱
k-Means的5个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:
图中显示:k-Means中选择3个cluster时,WCSS距离值改变的趋势基本不明显,于是选择K=3
参考博文:
https://blog.csdn.net/u011730199/article/details/78108263
https://www.cnblogs.com/sharon123/p/6828853.html
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