Fuzzy c-Means Classifier Based on Iteratively Reweighted Least Square Technique

抄録

A novel membership function and a fuzzy clustering derived from a viewpoint of iteratively reweighted least square (IRLS) techniques resolves the problem of singularity in the regular fuzzy c-means (FCM) clustering. A FCM classifier using the membership function and Mahalanobis distances makes class memberships of outliers less clear-cut, which thus resolve the problem of classification based on normal populations or normal mixtures. Computational experiments show high classification performance and compression rate on several well-known benchmark data sets.