k-means是一種非監督 （從下圖 0 當中我們可以看到訓練數據并沒有標簽標注類別）的聚類算法：

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:?

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0.initial

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1.select centroids randomly? ?

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2.assign points

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?3.update centroids

?4.reassign points

?5.update centroids

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?6.reassign points

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7.iteration

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reference：

https://www.naftaliharris.com/blog/visualizing-k-means-clustering/

https://www.saedsayad.com/clustering_kmeans.htm

轉載請注明來源：https://www.cnblogs.com/lnas01/p/10347650.html

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轉載于:https://www.cnblogs.com/lnas01/p/10347650.html

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