Divide-conquer method for improving possibilistic c-means

Abstract

Possibilistic c‐means (PCM) was proposed to overcome the problem of the noise sensitivity of fuzzy c ‐means, but its performance highly depends on the initialisation of cluster centres and often is degraded due to producing coincident clusters or close centres. To tackle these defects of PCM, a divide‐conquer method which not only provides appropriate cluster centres but also yields pre‐clustered and un‐clustered data information which are used to overcome the coincident or close clustering problem is presented. Experiment results on a simulated magnetic resonance brain image data corrupted by noise and bias‐field shows that the proposed method has a better clustering performance than conventional PCM clustering methods.