%KMEANS k-means clustering%%   [LABELS,A] = KMEANS(A,K,MAXIT,INIT,FID)%% INPUT%  A       Matrix or dataset%  K       Number of clusters to find (optional; default: 2)%  MAXIT   maximum number of iterations (optional; default: 50)%  INIT    Labels for initialisation, or%          'rand'     : take at random K objects as initial means, or%          'kcentres' : use KCENTRES for initialisation (default)%  FID     File ID to write progress to (default [], see PRPROGRESS)%% OUTPUT%  LABELS  Cluster assignments, 1..K%  A       Dataset with original data and labels LABELS% % DESCRIPTION% K-means clustering of data vectors in A. %% SEE ALSO% DATASETS, HCLUST, KCENTRES, MODESEEK, EMCLUST, PRPROGRESS% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: kmeans.m,v 1.14 2005/10/05 09:30:45 duin Exp $function [assign,a] = kmeans(a,kmax,maxit,init,fid)	prtrace(mfilename);	n_ini = 100;				% Maximum size of subset to use for initialisation.	if (nargin < 2) | isempty(kmax)		kmax = 2; 		%prwarning(3,'No K supplied, assuming K = 2.');	end	if nargin < 3 | isempty(maxit)		maxit = 50;	end	if nargin < 4 | isempty(init)		init = 'kcentres';	end	if nargin < 5		fid = [];	end	% Create dataset with all equal labels and no priors.	a = dataset(a);	islabtype(a,'crisp');	m = size(a,1);   a=set(a,'labels',ones(m,1),'lablist',[1:kmax]','prior',[]); % for speed		n_ini = max(n_ini,kmax*5);  % initialisation needs sufficient samples 		prprogress(fid,'\nkmeans clustering procedure\n')	% Initialise by performing KCENTRES on...	if (size(init,1) == 1) & strcmp(init,'kcentres') & (m > n_ini) 		%prwarning(2,'Initializing by performing KCENTRES on subset of %d samples.', n_ini);		b = +gendat(a,n_ini); % ... a random subset of A.		d = +distm(b);		assign = kcentres(d,kmax,[],fid);		w = nmc(dataset(b,assign)); % Initial partition W and assignments ASSIGN.	elseif (size(init,1) == 1) & strcmp(init,'kcentres')		%prwarning(2,'Initializing by performing KCENTRES on training set.');		d = +distm(a);    % ... the entire set A.		assign = kcentres(d,kmax,[],fid);		w = nmc(dataset(a,assign)); % mapping trained on the complete dataset	elseif (size(init,1) == 1) & strcmp(init,'rand')		%prwarning(2,'Initializing by randomly selected objects');		R = randperm(m);		w = nmc(dataset(a(R(1:kmax),:),[1:kmax]')); % mapping trained on kmax random samples	elseif (size(init,1) == m)		assign = renumlab(init);		kmax = max(assign);		%prwarning(2,'Initializing by given labels, k = %i',kmax);		w = nmc(dataset(a,assign));	else		error('Wrong initialisation supplied')	end	assign = labeld(a*w);						a = dataset(a,assign);	%tmp_assign = zeros(m,1); % Allocate temporary array.	% Main loop, while assignments change	it=1; % number of iterations	ndif = 1;	while (it<maxit) & (ndif > 0)		tmp_assign = assign;     % Remember previous assignments.		a = setnlab(a,assign);			w = a * nmc;   % Re-partition the space by assigning samples to nearest mean.		assign = a * w * labeld;  % Re-calculate assignments.		it = it+1; % increase the iteration counter		ndif = sum(tmp_assign ~= assign);		prprogress(fid,'  iter: %i changes: %i\n',it,ndif); 	end		if it>=maxit		%prwarning(1,['No convergence reached before the maximum number of %d iterations passed. ' ...			%'The last result was returned.'], maxit);	end	prprogress(fid,'kmeans finished\n')	return;