Fuzzy-CCM: A context-sensitive approach to fuzzy modeling

Issue title: Hybrid Fuzzy Models

Guest editors: José M. Benítezx, Salvador Garcíay, Santi Caballéz and Ángel Alejandro Juanz

Article type: Research Article

Authors: Nogueira, Tatiane Marques^{; *} | de Arruda Camargo, Heloisa

Affiliations: Computer Science Department, Federal University of São Carlos (UFSCar), São Carlos SP 13565-905, Brazil | [x] Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain | [y] Department of Computer Science, University of Jaén, Jaén, Spain | [z] Open University of Catalonia, Barecelona, Spain

Correspondence: [*] Corresponding author. E-mail: tatiane_nogueira@dc.ufscar.br

Abstract: The objective of this work is to present a fuzzy modeling method based on the conditional fuzzy clustering algorithm – the Fuzzy-CCM (Fuzzy Conditional Clustering Modeling) method. The balance between interpretability and accuracy of fuzzy rules is addressed by means of the definition of contexts formed with a small number of input variables and the generation of clusters conditioned by the context defined. The rules are generated in a different format which have linguistic variables with their values as well as groups. Some experiments have been run using different domains in order to validate the proposed approach and to compare the results with the ones obtained with the Wang&Mendell; and FCMeans methods. The advantages of the method, the experiments and the results obtained are discussed.

Keywords: Conditional clustering, fuzzy rules, rules interpretability, fuzzy modeling

DOI: 10.3233/HIS-2010-0103

Journal: International Journal of Hybrid Intelligent Systems, vol. 7, no. 1, pp. 33-43, 2010