Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation

This paper presents a fuzzy clustering algorithm, called the alternative fuzzy c-numbers (AFCN) clustering algorithm, for LR-type fuzzy numbers based on an exponential-type distance function. On the basis of the gross error sensitivity and influence function, this exponential-type distance is claimed to be robust with respect to noise and outliers. Hence, the AFCN clustering algorithm is more robust than the fuzzy c-numbers (FCN) clustering algorithm presented by Yang and Ko (Fuzzy Sets and Systems 84 (1996) 49). Some numerical experiments were performed to assess the performance of FCN and AFCN. Numerical results clearly indicate AFCN to be superior in performance to FCN. Finally, we apply the FCN and AFCN algorithms to real data. The experimental results show the superiority of AFCN in Taiwanese tea evaluation.