Probabilistic clustering algorithms for fuzzy rules decomposition
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abstract
The fuzzy c-means (FCM) clustering algorithm is the best known and used
method in fuzzy clustering and is generally applied to well defined set of data. In this
paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied
to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one
for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to
the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be
conveniently linked into a Parallel Collaborative Structures.
