kmeans.m

machine vision 工具箱

%KMEANS	K-means clustering
%
%	[c s] = KMEANS(x,k)
%	[c s] = KMEANS(x,k, x0)
%	[c s] = KMEANS(x,k, x0)
%
%	K-means clustering for one dimensional data, x.  k is the number of
%	clusters, and x0 if given is the inital centroid for the clusters.
%
%	On return c[i] is the centroid of the i'th cluster.  s is a vector 
%	of length equal to x, whose value indicates which cluster the 
%	corresponding element of x belongs to.
%
% REF: Tou and Gonzalez, Pattern Recognition Principles, pp 94
%
%
%	Copyright (c) Peter Corke, 1999  Machine Vision Toolbox for Matlab

%		pic 7/92
%
function [c,s] = kmeans(x, K, c0)
	deb = 0;

	if nargin == 2,
		%
		% if no initial clusters are given spread the
		% centers evenly over the range min to max.
		%
		z = [0:K-1]/(K-1)*(max(x)-min(x)) + min(x);
	end
	if length(z) ~= K,
		error('initial cluster length should be k')
	end

	%
	% step 1
	%
	zp = z;
	s = zeros(size(x));
	n = length(x);

	iterating = 1;
	k = 1;
	iter = 0;
	while iterating,
		iter = iter + 1;

		%
		% step 2
		%
		for l=1:n,	% for each point l=1..n
			[y,ind] = min(abs(x(l) - z));
			s(l) = ind;	% assign index of closest set
		end
			
		%
		% step 3
		%
		for j=1:K
			zp(j) = mean( x(s==j) );
		end

		%
		% step 4
		%
		nm = norm(z - zp);
		if deb>0,
			nm
		end
		if nm == 0,
			iterating = 0;
		end
		z = zp;
		if deb>0,
			plot(z);
			pause(.1);
		end
	end
	if deb>0,
		disp('iterations ');
		disp(iter);
	end
	c = z;