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.