Supervised fuzzy clustering for rule extraction

The paper is concerned with the application of orthogonal transforms and fuzzy clustering to the extraction of fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with respect to fitting the data. Clustering takes place in the product space of systems in and outputs, and each cluster corresponds to a fuzzy IF-THEN rule. By initializing the clustering with an overestimated number of clusters, and subsequently remove less important clusters (rules) as the clustering progresses, it is sought to obtain a suitable partition of the data in an automated. The approach is studied for the fuzzy c-means algorithm and applied to a function approximation example known from the literature.