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 initializationif isstruct(sD),   if isfield(sD,'data'), D = sD.data;   else D = sD.codebook;   endelse D = sD; end[dlen dim] = size(D);if nargin < 2 | isempty(n_max) | isnan(n_max), n_max = ceil(sqrt(dlen)); endif nargin < 3 | isempty(c_max) | isnan(c_max), c_max = 5; endif nargin < 4 | isempty(verbose) | isnan(verbose), verbose = 0; endcenters   = 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 evaluatedm = zeros(1,dim);for i=1:dim, m(i)=mean(D(isfinite(D(:,i)),i)); endcenters{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); endfor 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  endendreturn;