kmeans_clusters

# kmeans_clusters - err(vector squared sum of errors for each...

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SOM Toolbox Online documentation http://www.cis.hut.fi/projects/somtoolbox/ 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
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Unformatted text preview: 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 SOM_KMEANS. [ SOM Toolbox online doc ]...
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