Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Clustering is one of the important tools to analize data structure, and fuzzy c-means (FCM) is a typical method of clustering. By the way, numerical data include some errors in many cases. It is difficult to classify such data. To solve such problems, Endo et al. have introduced the concept of tolerance and constructed the algorithm of fuzzy c-means for data with tolerance(FCMT). In those algorithms, constraints for tolerance play an important role. In this paper, we will try to remove the constraints by introducing the penalty term instead of those. Moreover, L1-norm based FCM has been constructed. We will try to compare L1-norm based method with L2-norm based method.
