comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/kmeans_clusters.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [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 SOM_KMEANS.
+
+% References: 
+%   Jain, A.K., Dubes, R.C., "Algorithms for Clustering Data", 
+%   Prentice Hall, 1988, pp. 96-101.
+%
+%   Davies, D.L., Bouldin, D.W., "A Cluster Separation Measure", 
+%   IEEE Transactions on Pattern Analysis and Machine Intelligence, 
+%   vol. PAMI-1, no. 2, 1979, pp. 224-227.
+%
+%   Vesanto, J., Alhoniemi, E., "Clustering of the Self-Organizing
+%   Map", IEEE Transactions on Neural Networks, 2000.
+
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
+% Copyright (c) by Esa Alhoniemi
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% ecco 301299 juuso 020200 211201
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% input arguments and initialization
+
+if isstruct(sD), 
+  if isfield(sD,'data'), D = sD.data; 
+  else D = sD.codebook; 
+  end
+else D = sD; 
+end
+[dlen dim] = size(D);
+
+if nargin < 2 | isempty(n_max) | isnan(n_max), n_max = ceil(sqrt(dlen)); end
+if nargin < 3 | isempty(c_max) | isnan(c_max), c_max = 5; end
+if nargin < 4 | isempty(verbose) | isnan(verbose), verbose = 0; end
+
+centers   = cell(n_max,1); 
+clusters  = cell(n_max,1);
+ind       = zeros(1,n_max)+NaN;
+errors    = zeros(1,n_max)+NaN;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% action
+
+% the case k=1 is trivial, but Davies-Boulding index cannot be evaluated
+m = zeros(1,dim);
+for i=1:dim, m(i)=mean(D(isfinite(D(:,i)),i)); end
+centers{1} = m;
+clusters{1} = ones(dlen,1);
+[dummy qerr] = som_bmus(m,D);
+errors(1) = sum(qerr.^2);
+ind(1) = NaN; 
+
+if verbose, fprintf(2,'Doing k-means for 2-%d clusters\n',n_max); end
+
+for i = 2:n_max, % number of clusters
+
+  % make k-means with k=i for c_max times and select the best based
+  % on sum-of-squared errors (SSE)
+  best = realmax;  
+  for j = 1:c_max     % run number j for cluster i      
+    if verbose,
+      fprintf('%d/%d clusters, k-means run %d/%d\r', i, n_max,j, c_max);
+    end      
+    [c, k, err] = som_kmeans('batch', D, i, 100, 0);
+    if err < best, k_best = k'; c_best = c; best = err; end
+    % ' added in k_best = k'; by kr 1.10.02
+  end
+  if verbose, fprintf(1, '\n');  end
+
+  % store the results  
+  centers{i}  = c_best;
+  clusters{i} = k_best;
+  errors(i)   = best;
+%  ind(i)      = db_index(D, c_best, k_best, 2); wrong version in somtbx ??
+  ind(i)      = db_index(D, k_best, c_best, 2); % modified by kr 1.10.02
+
+  % if verbose mode, plot the index & SSE
+  if verbose
+    subplot(2,1,1), plot(ind), grid
+    title('Davies-Bouldin''s index')
+    subplot(2,1,2), plot(errors), grid
+    title('SSE')
+    drawnow
+  end
+end
+
+return; 
+
+
+