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https://doi.org/10.1109/icsmc....
Article . 2005 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2017
Data sources: DBLP
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Fuzzy clustering of fuzzy systems

Authors: Salgado, Paulo; Garrido, Paulo;

Fuzzy clustering of fuzzy systems

Abstract

A fuzzy system entirely characterizes one region of the input-output product space S = U /spl times/ V through a relation expressed by a set of fuzzy rules. Effectively, the fuzzy system establishes a fuzzy map, which assigns for each input fuzzy set in U an output fuzzy set in V. The partition of this product space may be made through the decomposition of the relation. The fuzzy clustering of fuzzy rules, here proposed, as well as clustering of data, leads to a fuzzy partition of the S space. The result is a set of fuzzy sub-systems, one for each cluster that will be conveniently linked in a new structure. This paper proposes a new recursive clustering algorithm for the partition of a fuzzy system into a hierarchical collaborative structure. The global response of the hierarchical collaborative structure is identical to the input fuzzy system.

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Portugal
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Keywords

Fuzzy system, Fuzzy clustering, Hierarchical model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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