%MYKMEANS K-means clustering % % [LABS,MEANS] = MYKMEANS(X,K) % % Place K centers in the data X using the k-means procedure. % Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org % Faculty EWI, Delft University of Technology % P.O. Box 5031, 2600 GA Delft, The Netherlands function [labs,means,err] = mykmeans(x,k,errtol) if nargin errtol = 1e-5; end % init: [n,d] = size(x); % use k random objects as initialization I = randperm(n); means = x(I(1:k),:); % label all objects: D = distm(x,means); [mn, labs] = min(D,[],2); % the reconstruction error: err = sum(mn); olderr = 10*err; % update the means until the error does not change while ((olderr-err)>errtol*err) % update the means: for i=1:k I = find(labs==i); if length(I)>0 means(i,:) = mean(x(I,:),1); end end % relabel all objects: D = sqeucldistm(x,means); [mn, labs] = min(D,[],2); % the error: olderr = err; err = sum(mn); end return
