%MODESEEK Clustering by modeseeking% % 	[labels,J] = modeseek(D,k)% % If D is a n*n distance matrix between object then a k-nn % modeseeking method is used to assign each object to its nearest % mode. The indices in J point to the modal objects. All objects % are labeled accordingly, which is returned in labels.% % 	[labels,J] = modeseek(distmap,A,k)% % Instead of D a distance computing untrained mapping (e.g. % proxm([],'d',2)) may be supplied for computing the distances between % objects in A. This prevents the computation of large distance % matrices.% % See also mappings, datasets, kmeans, hclust, kcentres% 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 Netherlandsfunction [lab,J] = modeseek(dist,a,k)if isa(dist,'mapping')	if nargin < 3, k = 10; end	[m,n] = size(a);else	if nargin < 2, k = 10;	else k = a; end	[m,n] = size(dist);	if m ~= n, error('Distance matrix should be square'); endendf = zeros(m,1);	    % densitiesJ = zeros(k,m);     % neighborsfor i = 1:m	if isa(dist,'mapping')		%d = feval(dist,a,a(i,:)); d = d(:);		d = a(i,:) * dist * a;	else		d = dist(:,i);	end	[dd,j] = sort(d);	f(i) = 1/dd(k);	J(:,i) = j(1:k);end[e,j] = max(reshape(f(J),size(J)));N = J(j+[0:k:k*(m-1)]);M = N(N);while any(M~=N)	N = M;	M = N(N);end[lab,J] = renumlab(M');