%MODESEEK Clustering by mode-seeking% % 	[LAB,J] = MODESEEK(D,K)% % INPUT%   D       Distance matrix or distance dataset (square)%   K       Number of neighbours to search for local mode (default: 10)%% OUTPUT%   LAB     Cluster assignments, 1..K%   J       Indices of modal samples%% DESCRIPTION% A K-NN modeseeking method is used to assign each object to its nearest mode.%% LITERATURE% Cheng, Y. "Mean shift, mode Seeking, and clustering", IEEE Transactions% on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799,% 1995.% % SEE ALSO% MAPPINGS, DATASETS, KMEANS, HCLUST, KCENTRES, PROXM% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands% $Id: modeseek.m,v 1.7 2004/09/30 07:35:41 duin Exp $function [assign,J] = modeseek (d,k)	prtrace(mfilename);	if (nargin < 2)		prwarning(1,'No k supplied, assuming k = 10'); 		k = 10;	end	[m,n] = size(d);	if (m ~= n), error('distance matrix should be square'); end	if (k < 2),  error('neighborhood size should be at least 2'); end	if (k > n),  error('k too large for this dataset'); end	[d,J] = sort(+d,1);		   % Find neighbours.	f = 1./(d(k,:)+realmin); % Calculate densities.	J(k+1:end,:) = [];   	   % Just retain indices of neighbours.	% Find indices of local modes in neighbourhood.	[dummy,I] = max(reshape(f(J),size(J)));	% Translate back to indices in all the data. N now contains the	% index of the nearest neighbour in the K-neighbourhood.	N = J(I+[0:k:k*(m-1)]);	% Re-assign samples to the sample their nearest neighbour is assigned to.	% Iterate until assignments don't change anymore. Samples that then point 	% to themselves are modes; all other samples point to the closest mode.	M = N(N);	while (any(M~=N))		N = M; M = N(N);	end	% Use renumlab to obtain assignments 1, 2, ... and the list of unique	% assignments (the modes).	[assign,J] = renumlab(M');return