The command for fuzzy c mean clustering is FCM. genfis3 uses FCM to build a fuzzy inference system.

@ Mr. Awad

I do not think that one can define the initial guesses for center of clusters in matlab built-in function FCM. You can run the FCM a number of times and save the best result.

In Matlab (R2012b) centers are determined on the basis of membership functions:

stepfcn.m:

mf = U.^expo; % MF matrix after exponential modification

center = mf*data./((ones(size(data, 2), 1)*sum(mf'))'); % new center

Initial values of U matrix are random:

fcn.m:

U = initfcm(cluster_n, data_n); % Initial fuzzy partition

initfcm.m:

U = rand(cluster_n, data_n);

col_sum = sum(U);

U = U./col_sum(ones(cluster_n, 1), :);

So you can modify stepfcn.m:

%adding last parameter iter

function [U_new, center, obj_fcn] = stepfcm(data, U, cluster_n, expo, iter)

...

mf = U.^expo; % MF matrix after exponential modification

if (iter ==1)

center = [ your data];

else

center = mf*data./((ones(size(data, 2), 1)*sum(mf'))'); % new center

end;

...

And modify fcm.m:

...

[U, center, obj_fcn(i)] = stepfcm(data, U, cluster_n, expo, i);

...

Good luck!

Thanks for all who answered the question. However, I believe the best way is to modify FCM module to accept input from another process. This way if I used an unsupervised technique which can provide initial cluster centers than the whole process becomes unsupervised

You could  the code  attached. A simple code of FCM for Matlab/Octave as a function. You could change the initial coordinates of cluster centers as you see fit.

good luck.