rng default;
X = [randn(10,2)+2*ones(10,2); randn(10,2)-2*ones(10,2)];
[Cntr,U] = fcm(X,2);
Cluster centers are
Cntr
-1.6498 -2.4173
2.7346 2.7327
Now look at the cluster membership for the first ten rows of X
U(:,1:10)
clearly X(1,:) belongs to cluster 2 because 0.8603 is much larger than 0.1397, X(2,:) even more so because 0.9293 is close to 1. So you would create an idx vector with a 2 for the first row of X and a 2 for the second row of X. You would continue on in that way.
If you look at:
U(:,11)
0.9454
0.0546
that means that X(11,:) belongs to cluster 1.
As the documentation clearly explains the closer the value to 0 the more unlike that cluster an observation is, the closer to 1 the more like it.
You have five clusters so you are going to have five rows in U, pick the largest value in those rows as the cluster. But of course, you might not have something clearly belonging to one cluster.
Ultimately you want to use the information in U to create a vector with the same number of rows as your input with a 1,2,3,4, or 5 as the element which indicates cluster membership.
To form the cluster membership vector, you can do something like:
X = [randn(10,2)+2*ones(10,2); randn(10,2)-2*ones(10,2)];
[Cntr,U] = fcm(X,2);
[Y,cidx] = max(U,[],1);
cidx = cidx';
Thank you for accepting my answer if I have helped you.
