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Divide-conquer method for improving possibilistic c-means

Divide-conquer method for improving possibilistic c-means

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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.


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