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My problem is that it is difficult to get the optimal cluster number by using k-means, so I thought of using a hierarchical algorithm to find the optimal cluster number. After defining my ideal classification I want to use this classification to find the centroids with k-means, without iteration.

data= rand(300,5);
D = pdist(data);
Z = linkage(D,'ward');
T = cluster(Z,'maxclust',6);

Now I want to use the clusters defined in vector T and the positions in to k-means algorithm without iterations. Can anyone give a tip how to do?

Thank you.

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If you are looking for the centroids given that you already clustered them in T, then you only need to compute the mean of data grouped according to T.

于 2013-03-04T18:38:51.840 回答