Regarding to the literature, there are fuzzy clustering and rough clustering approaches for soft clustering, but I am wonder if there are any other type of soft clustering available?
There is also credal clustering, which is based on the theory of belief functions. See the following papers:
T. Denoeux and M.-H. Masson. EVCLUS: Evidential Clustering of Proximity Data. IEEE Transactions on Systems, Man and Cybernetics B, Vol. 34, Issue 1, 95-109, 2004.
M.-H. Masson and T. Denoeux. ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition, Vol. 41, Issue 4, pages 1384– 1397, 2008.
V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CECM: Constrained Evidential C-Means algorithm. Computational Statistics and Data Analysis, Vol. 56, Issue 4, pages 894-914, 2012.
V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CEVCLUS: Evidential clustering with instance-level constraints for relational data. Soft Computing, Volume 18, Issue 7, pp 1321-1335, 2014.
Clustering by negotiation among software agents representing data is precise and rapid. It is done in real time and therefore suitable for dynamic environments. See book Managing Complexity by George Rzevski and Petr Skobelev, WIT Press, 2014, available from Amazon.
Hello, Shadowed C Means is other type of soft-clustering algorithm. You can look for it over the internet.
Regards
Ramiro
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