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

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