I have a D-dimensional dataset composed of exactly two clusters (this is known) for which I have no labels; the clusters can potentially be wildly imbalanced.
I'm after a soft (or fuzzy) clustering method to assign probabilities to each element of belonging to either cluster. So far I've been able to come up with basically two:
Then there are methods that apply hard clustering that could perhaps be softened by re-running varying the inputs (and averaging all the iterations?):
And finally there's also those methods that I'm not sure whether they can be applied in an unsupervised way at all:
Am I missing some method? Did I miss-classify any of the above? Is any method more suited to my particular issue than the rest?
Any insight will be much appreciatted.