Determining the optimal number of clusters in Kmeans technique

I have a matrix like "A". I want to cluster its data using K-Means method.

A=[45  58  59
46  76  53
57  65  71
40  55  59
25  35  42
34  51  74
46  90  53
46  63  60
33  50  78
53  57  60
31  28  72
49  49  53
76  88  82
34  100  198
35  35  35];

I used the following command to cluster data.

[Data_clustred, c]= kmeans(A,num_cluster);

by the way, knowing the optimal number of cluster is neccessary to me.

Is there any criteria that determines the optimal numbers of clusters? if so, How can I write its programm.

any help whould be appreciated. Thanks in advance.

Answer 1

[Walter Roberson]

http://www.mathworks.com/matlabcentral/answers/34623-how-to-determine-the-optimum-number-of-cluster-use-k-mean-clustering

[som]

Thanks for your answer. Do you have a complete MATLAB code for K-means algorithm? I mean a code that be able to import a data set, do clustering and export cluster data? Thanks in advance

Answer 2

[min ho lee]

hi, You can use this article.

[som]

Thanks for the file. Can I ask you to email the pdf file of your answer i.e. "Community detection by signaling on complex networks.pdf" to the following email: [ [e-mail]] Thanks in advance

Answer 3

[kira]

old question, but I just found a way myself looking at matlab documentation:

klist=2:n;%the number of clusters you want to try
myfunc = @(X,K)(kmeans(X, K));
eva = evalclusters(net.IW{1},myfunc,'CalinskiHarabasz','klist',klist)
classes=kmeans(net.IW{1},eva.OptimalK);