Obtaining a fuzzy classification rule system from a non-supervised clustering

The fuzzy classification systems have been broadly used to solve control and decision-making problem. However, its design is complex, even when having a human expert assistance. This paper presents a new strategy capable of automatically defining the corresponding Fuzzy Classification Rule System from a non-supervised clustering of the available data. Its application to three data sets of the UCI repository has given quite satisfactory results.