How to define a suitbale number of centroids for fuzzy c-means?
You can try the exact same heuristics you would use for k-means.

But usually, you would do this:


try some parameters by experience
run the algorithm
carefully inspect the result
go back to 1 and try other parameters until you have found something interesting or are tired of trying


There is no "correct" solution in clustering.

It's about discovering some new pattern and you cannot out the "new" into an optimization equation. The point is to try, and try again. It is not a drop-in replacement for classification.