Modelagem fuzzy usando agrupamento condicional

Abstract

The combination of fuzzy systems with clustering algorithms has great acceptance in the scientific community mainly due to its adherence to the advantage balance principle of computational intelligence, in which different methodologies collaborate with each other potentializing the usefulness and applicability of the resulting systems. Fuzzy Modeling using clustering algorithms presents the transparency and comprehensibility typical of the linguistic fuzzy systems at the same time that benefits from the possibilities of dimensionality reduction by means of clustering. In this work is presented the Fuzzy-CCM method (Fuzzy Conditional Clustering based Modeling) which consists of a new approach for Fuzzy Modeling based on the Fuzzy Conditional Clustering algorithm aiming at providing new means to address the topic of interpretability of fuzzy rules bases. With the Fuzzy-CCM method the balance between interpretability and accuracy of fuzzy rules is dealt with through the definition of contexts defined by a small number of input variables and the generation of clusters induced by these contexts. The rules are generated in a different format, with linguistic variables and clusters in the antecedent. Some experiments have been carried out using different knowledge domains in order to validate the proposed approach by comparing the results with the ones obtained by the Wang&Mendel and conventional Fuzzy C-Means methods. The theoretical foundations, the advantages of the method, the experiments and results are presented and discussed.