Mercurial > hg > camir-aes2014

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

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
rev   line source
wolffd@0 1 function [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose)
wolffd@0 2
wolffd@0 3 % KMEANS_CLUSTERS Clustering with k-means with different values for k.
wolffd@0 4 %
wolffd@0 5 % [c, p, err, ind] = kmeans_clusters(sD, [n_max], [c_max], [verbose])
wolffd@0 6 %
wolffd@0 7 % [c, p, err, ind] = kmeans_clusters(sD);
wolffd@0 8 %
wolffd@0 9 % Input and output arguments ([]'s are optional):
wolffd@0 10 % D (struct) map or data struct
wolffd@0 11 % (matrix) size dlen x dim, the data
wolffd@0 12 % [n_max] (scalar) maximum number of clusters, default is sqrt(dlen)
wolffd@0 13 % [c_max] (scalar) maximum number of k-means runs, default is 5
wolffd@0 14 % [verbose] (scalar) verbose level, 0 by default
wolffd@0 15 %
wolffd@0 16 % c (cell array) c{i} contains cluster centroids for k=i
wolffd@0 17 % p (cell array) p{i} contains cluster indeces for k=i
wolffd@0 18 % err (vector) squared sum of errors for each value of k
wolffd@0 19 % ind (vector) Davies-Bouldin index value for each clustering
wolffd@0 20 %
wolffd@0 21 % Makes a k-means to the given data set with different values of
wolffd@0 22 % k. The k-means is run multiple times for each k, and the best of
wolffd@0 23 % these is selected based on sum of squared errors. Finally, the
wolffd@0 24 % Davies-Bouldin index is calculated for each clustering.
wolffd@0 25 %
wolffd@0 26 % For example to cluster a SOM:
wolffd@0 27 % [c, p, err, ind] = kmeans_clusters(sM); % find clusterings
wolffd@0 28 % [dummy,i] = min(ind); % select the one with smallest index
wolffd@0 29 % som_show(sM,'color',{p{i},sprintf('%d clusters',i)}); % visualize
wolffd@0 30 % colormap(jet(i)), som_recolorbar % change colormap
wolffd@0 31 %
wolffd@0 32 % See also SOM_KMEANS.
wolffd@0 33
wolffd@0 34 % References:
wolffd@0 35 % Jain, A.K., Dubes, R.C., "Algorithms for Clustering Data",
wolffd@0 36 % Prentice Hall, 1988, pp. 96-101.
wolffd@0 37 %
wolffd@0 38 % Davies, D.L., Bouldin, D.W., "A Cluster Separation Measure",
wolffd@0 39 % IEEE Transactions on Pattern Analysis and Machine Intelligence,
wolffd@0 40 % vol. PAMI-1, no. 2, 1979, pp. 224-227.
wolffd@0 41 %
wolffd@0 42 % Vesanto, J., Alhoniemi, E., "Clustering of the Self-Organizing
wolffd@0 43 % Map", IEEE Transactions on Neural Networks, 2000.
wolffd@0 44
wolffd@0 45 % Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi
wolffd@0 46 % Copyright (c) by Esa Alhoniemi
wolffd@0 47 % http://www.cis.hut.fi/projects/somtoolbox/
wolffd@0 48
wolffd@0 49 % ecco 301299 juuso 020200 211201
wolffd@0 50
wolffd@0 51 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 52 %% input arguments and initialization
wolffd@0 53
wolffd@0 54 if isstruct(sD),
wolffd@0 55 if isfield(sD,'data'), D = sD.data;
wolffd@0 56 else D = sD.codebook;
wolffd@0 57 end
wolffd@0 58 else D = sD;
wolffd@0 59 end
wolffd@0 60 [dlen dim] = size(D);
wolffd@0 61
wolffd@0 62 if nargin < 2 | isempty(n_max) | isnan(n_max), n_max = ceil(sqrt(dlen)); end
wolffd@0 63 if nargin < 3 | isempty(c_max) | isnan(c_max), c_max = 5; end
wolffd@0 64 if nargin < 4 | isempty(verbose) | isnan(verbose), verbose = 0; end
wolffd@0 65
wolffd@0 66 centers = cell(n_max,1);
wolffd@0 67 clusters = cell(n_max,1);
wolffd@0 68 ind = zeros(1,n_max)+NaN;
wolffd@0 69 errors = zeros(1,n_max)+NaN;
wolffd@0 70
wolffd@0 71 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0 72 %% action
wolffd@0 73
wolffd@0 74 % the case k=1 is trivial, but Davies-Boulding index cannot be evaluated
wolffd@0 75 m = zeros(1,dim);
wolffd@0 76 for i=1:dim, m(i)=mean(D(isfinite(D(:,i)),i)); end
wolffd@0 77 centers{1} = m;
wolffd@0 78 clusters{1} = ones(dlen,1);
wolffd@0 79 [dummy qerr] = som_bmus(m,D);
wolffd@0 80 errors(1) = sum(qerr.^2);
wolffd@0 81 ind(1) = NaN;
wolffd@0 82
wolffd@0 83 if verbose, fprintf(2,'Doing k-means for 2-%d clusters\n',n_max); end
wolffd@0 84
wolffd@0 85 for i = 2:n_max, % number of clusters
wolffd@0 86
wolffd@0 87 % make k-means with k=i for c_max times and select the best based
wolffd@0 88 % on sum-of-squared errors (SSE)
wolffd@0 89 best = realmax;
wolffd@0 90 for j = 1:c_max % run number j for cluster i
wolffd@0 91 if verbose,
wolffd@0 92 fprintf('%d/%d clusters, k-means run %d/%d\r', i, n_max,j, c_max);
wolffd@0 93 end
wolffd@0 94 [c, k, err] = som_kmeans('batch', D, i, 100, 0);
wolffd@0 95 if err < best, k_best = k'; c_best = c; best = err; end
wolffd@0 96 % ' added in k_best = k'; by kr 1.10.02
wolffd@0 97 end
wolffd@0 98 if verbose, fprintf(1, '\n'); end
wolffd@0 99
wolffd@0 100 % store the results
wolffd@0 101 centers{i} = c_best;
wolffd@0 102 clusters{i} = k_best;
wolffd@0 103 errors(i) = best;
wolffd@0 104 % ind(i) = db_index(D, c_best, k_best, 2); wrong version in somtbx ??
wolffd@0 105 ind(i) = db_index(D, k_best, c_best, 2); % modified by kr 1.10.02
wolffd@0 106
wolffd@0 107 % if verbose mode, plot the index & SSE
wolffd@0 108 if verbose
wolffd@0 109 subplot(2,1,1), plot(ind), grid
wolffd@0 110 title('Davies-Bouldin''s index')
wolffd@0 111 subplot(2,1,2), plot(errors), grid
wolffd@0 112 title('SSE')
wolffd@0 113 drawnow
wolffd@0 114 end
wolffd@0 115 end
wolffd@0 116
wolffd@0 117 return;
wolffd@0 118
wolffd@0 119
wolffd@0 120