function irisfcm
% IRISFCM Fuzzy c-means clustering demo (4-D).
% This is a script illustrating how to use fuzzy c-means routines for
% IRIS data clustering, with on-line animation.
%
% See also DISTFCM, INITFCM, FCMDEMO, STEPFCM, FCM.
% R. Jang, 12-12-94.
% Copyright 1994-2002 The MathWorks, Inc.
% $Revision: 1.7 $ $Date: 2002/04/14 22:16:05 $
load iris.dat % Load data
class1 = iris(find(iris(:,5)==1), :); % data for class 1
class2 = iris(find(iris(:,5)==2), :); % data for class 2
class3 = iris(find(iris(:,5)==3), :); % data for class 3
data = iris; % whole data set to be clustered
data_n = size(data, 1); % number of data
expo = 2.0; % Exponent for U
cluster_n = 3; % Number of clusters
max_iter = 100; % Max. iteration
min_impro = 1e-6; % Min. improvement
obj_fcn = zeros(max_iter, 1); % Array for objective function
digitH = zeros(cluster_n, 6); % Array for handles of cluster centers
U = initfcm(cluster_n, data_n); % Initial fuzzy partition
[U, center] = stepfcm(data, U, cluster_n, expo);% Initial cluster centers
% Project data to 2-D and plot them
mark = '.';
seq = [1 2; 1 3; 1 4; 2 3; 2 4; 3 4];
for j = 1:6,
x = seq(j, 1); y = seq(j, 2); % inputs selected for plot
subplot(2,3,j);
h = plot([class1(:,x) class2(:,x) class3(:,x)], ...
[class1(:,y) class2(:,y) class3(:,y)], mark);
set(h, 'markersize', 8);
xlabel(['x' int2str(x)]); ylabel(['x' int2str(y)]);
% Initialize centers
for k = 1:cluster_n,
digitH(k,j) = text(center(k, seq(j,1)), ...
center(k,seq(j,2)), int2str(k));
set(digitH(k,j), 'erase', 'xor', 'horizon', 'center');
end
end
% Main loop
for i = 1:max_iter,
[U, center, obj_fcn(i)] = stepfcm(data, U, cluster_n, expo);
fprintf('Iteration count = %d, obj. fcn = %f\n', i, obj_fcn(i));
if i > 1, % check termination condition
if abs(obj_fcn(i) - obj_fcn(i-1)) < min_impro, break; end,
end
% refresh centers for animation
for j = 1:6,
for k = 1:cluster_n,
set(digitH(k,j), 'pos', center(k, seq(j,:)));
end
end
drawnow;
end
% Plot of objection function
iter_n = i; % Actual number of iterations
figure; % New figure
obj_fcn(iter_n+1:max_iter) = [];% Delete unused elements
plot(obj_fcn); axis([1 i min(obj_fcn) max(obj_fcn)]);
title('Objective Function for IRIS Data Clustering');
xlabel('Numbers of Iterations');
