Fuzzy c-Means Classifier for Incomplete Data Sets with Outliers and Missing Values

Abstract

A novel membership function and a fuzzy clustering approach derived from a viewpoint of iteratively reweighted least square (IRLS) techniques resolve the problem of singularity in the regular fuzzy c-means (FCM) clustering. An 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. The ways of handling singular covariance matrices and missing values are also furnished, which improve the generalization capability of the classifier. Computational experiments show high classification performance on several well-known benchmark data sets