Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Clustering is one of the unsupervised classification and fuzzy c-means (FCM) is one of the typical technique of fuzzy clustering. Endo et al. have introduced the concept of tolerance and constracted the algorithm of FCM for data with tolerance (FCM-T) to handle uncertainties with data. In the algorithm, the constraints for tolerance vectors are used. In this paper, we will try to get rid of the constraints by introducing the penalty term instead of there. On the other hand, the dissimilarity of FCM is defined as the squared L2-norm. Moreover, L1-norm based methods are also constructed. L1-norm methods can calculate results rapidly. In this paper, we will consider L1-norm based FCM.
