A new hybrid c-means clustering model

Abstract

Earlier we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM imposes a constraint on the sum of typicalities over a cluster that leads to unrealistic typicality values for large data sets. Here we propose a new model called possibilistic fuzzy c-means (PFCM). PFCM produces memberships and possibilities simultaneously, along with the cluster centers. PFCM addresses the noise sensitivity defect of FCM, overcomes the coincident clusters problem of possibilistic c-means (PCM) and eliminates the row sum constraints of FPCM. Our numerical examples show that PFCM compares favorably to all of the previous models.