kmeans_clusters

[centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)

 KMEANS_CLUSTERS Clustering with k-means with different values for k.

 [c, p, err, ind] = kmeans_clusters(sD, [n_max], [c_max], [verbose])

   [c, p, err, ind] = kmeans_clusters(sD);
  
  Input and output arguments ([]'s are optional):
   D         (struct) map or data struct
             (matrix) size dlen x dim, the data 
   [n_max]   (scalar) maximum number of clusters, default is sqrt(dlen)
   [c_max]   (scalar) maximum number of k-means runs, default is 5
   [verbose] (scalar) verbose level, 0 by default

   c         (cell array) c{i} contains cluster centroids for k=i
   p         (cell array) p{i} contains cluster indeces for k=i
   err       (vector) squared sum of errors for each value of k
   ind       (vector) Davies-Bouldin index value for each clustering

 Makes a k-means to the given data set with different values of
 k. The k-means is run multiple times for each k, and the best of
 these is selected based on sum of squared errors. Finally, the
 Davies-Bouldin index is calculated for each clustering. 

 For example to cluster a SOM: 
    [c, p, err, ind] = kmeans_clusters(sM); % find clusterings
    [dummy,i] = min(ind); % select the one with smallest index
    som_show(sM,'color',{p{i},sprintf('%d clusters',i)}); % visualize
    colormap(jet(i)), som_recolorbar % change colormap
  
 See also KMEANS.