📄 kmeans_clusters.html
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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / kmeans_clusters </title></head><body bgcolor=#f0f0f0><table border=0 width="100%" cellpadding=0 cellspacing=0><tr><td valign=baseline><font size=+2>SOM Toolbox</font></td><td valign=baseline align=center><a href="somtoolbox.html">Online documentation</td><td valign=baseline align=right><a href="http://www.cis.hut.fi/projects/somtoolbox/" target="_top">http://www.cis.hut.fi/projects/somtoolbox/</a></td></tr></table><hr><H1> kmeans_clusters </H1><P><B> [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)</B></P><PRE> 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 SOM_KMEANS.</PRE><p><hr><br><center>[ <a href="somtoolbox.html">SOM Toolbox online doc</a> ]</center><br><!-- Last updated: May 30 2002 --></body></html>⌨️ 快捷键说明
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