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I have a Fuzzy-C means function, is there a pro to explain it to me?

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Diboun Mohsen Djawad
function [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum)
% demo
% img = double(imread('brain.tif'));
% clusterNum = 3;
% [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum );
% figure;
% subplot(2,2,1); imshow(img,[]);
% for i=1:clusterNum
% subplot(2,2,i+1);
% imshow(Unow(:,:,i),[]);
% end
if nargin < 2
clusterNum = 8; % number of cluster
end
[row, col] = size(img);
expoNum = 2; % fuzzification parameter
epsilon = 0.001; % stopping condition
mat_iter = 100; % number of maximun iteration
rng('default');
Upre = rand(row, col, clusterNum);
dep_sum = sum(Upre, 3);
dep_sum = repmat(dep_sum, [1,1, clusterNum]);
Upre = Upre./dep_sum;
center = zeros(clusterNum,1);
for i=1:clusterNum
center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end
pre_obj_fcn = 0;
for i=1:clusterNum
pre_obj_fcn = pre_obj_fcn + sum(sum((Upre(:,:,i) .*img - center(i)).^2));
end
fprintf('Initial objective fcn = %f\n', pre_obj_fcn);
for iter = 1:mat_iter
Unow = zeros(size(Upre));
for i=1:row
for j=1:col
for uII = 1:clusterNum
tmp = 0;
for uJJ = 1:clusterNum
disUp = abs(img(i,j) - center(uII));
disDn = abs(img(i,j) - center(uJJ));
tmp = tmp + (disUp/disDn).^(2/(expoNum-1));
end
Unow(i,j, uII) = 1/(tmp);
end
end
end
now_obj_fcn = 0;
for i=1:clusterNum
now_obj_fcn = now_obj_fcn + sum(sum((Unow(:,:,i) .*img - center(i)).^2));
end
fprintf('Iter = %d, Objective = %f\n', iter, now_obj_fcn);
if max(max(max(abs(Unow-Upre))))<epsilon || abs(now_obj_fcn - pre_obj_fcn)<epsilon
break;
else
Upre = Unow.^expoNum;
for i=1:clusterNum
center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end
pre_obj_fcn = now_obj_fcn;
end
end

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