A fuzzy clustering approach for mining diagnostic rules

In this paper an approach for automatic discovery of transparent diagnostic rules from data is proposed. The approach is based on a fuzzy clustering technique that is defined by three sequential steps. First, our Crisp Double Clustering algorithm is applied on available symptoms measurements, to provide a set of representative multidimensional prototypes that are further clustered onto each one-dimensional projection. The resulting clusters are used in the second step, where a set of fuzzy relations are defined in terms of transparent fuzzy sets. As a final step, the derived fuzzy relations are employed to define a set of fuzzy rules, which establish the knowledge base of a fuzzy inference system that can be used for fuzzy diagnosis. The approach has been applied to the Aachen Aphasia dataset as a real-world benchmark and compared with related work.