Clustering algorithms for fuzzy rules decomposition
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abstract
This paper presents the development, testing
and evaluation of generalized Possibilistic
fuzzy c-means (FCM) algorithms applied to
fuzzy sets. Clustering is formulated as a
constrained minimization problem, whose
solution depends on the constraints imposed
on the membership function of the cluster and
on the relevance measure of the fuzzy rules.
This fuzzy clustering of fuzzy rules 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 Hierarchical
Prioritized Structures.
