Pattern classification problems and fuzzy sets

A unified presentation of classical clustering algorithms is proposed both for the hard and fuzzy pattern classification problems. Based on two types of objective functions, a new method is presented and compared with the procedures of Dunn and Ruspini. In order to determine the best, or more natural number of fuzzy clusters, two coefficients that measure the “degree of non-fuzziness” of the partition are proposed. Numerous computational results are shown.