Clustering algorithms for fuzzy rules decomposition
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 a...
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| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2010
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| Texto completo: | http://hdl.handle.net/10198/2757 |
| País: | Portugal |
| Oai: | oai:bibliotecadigital.ipb.pt:10198/2757 |
| Resumo: | 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. |
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