Context-Sensitive Clustering in the Design of Fuzzy Models

This work presents a hybrid fuzzy modeling approach based on the conditional fuzzy clustering algorithm, that aims to provide new means to handle the issue of interpretability of the rule base. 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.