Unsupervised soft clustering methods 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:


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*Fuzzy K-means

*Gaussian Mixture Models
Then there are methods that apply hard clustering that could perhaps be softened by re-running varying the inputs (and averaging all the iterations?):


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*DBSCAN

*Hierarchical
And finally there's also those methods that I'm not sure whether they can be applied in an unsupervised way at all:


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*Random Forests

*Mean Shift

*BIRCH
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.
 A: UPDATE
(Looking back on the OP, I would recommend running FKM and GMM, then try to publish the analysis based on that, or give a talk based on use of FKM/GMM.  There is nothing wrong with use of FKM/GMM for your OP.  You may be "missing" a lot other unknown/unpopular methods which develop probabilities -- so construction of list that misses nothing would become open-ended).
Only fuzzy k-means provides "membership function" values for each object, and GMM provides cluster-specific probabilities, $P(cluster|x)$.  "Crisp" k-means cluster analysis (opposite of fuzzy, i.e., the typical k-means) can provide something like a cluster-specific probability if you assume that Euclidean distance to the closest centroid represents a probability. There may be more methods that do this, but generally speaking, most other unsupervised methods are based on distance metrics, not probabilities.  
I wouldn't get "hung up," i.e., interested on being exact about all the various unsupervised methods and which ones do and don't provide cluster-specific probabilities.  If you are writing a review, book chapter, or a report for school, you shouldn't ask this forum about it.  
Have you grasped the full literature on this topic?  You'd get a much better answer doing your own research.  
