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.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2017-02
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.53, no.3

ISSN
0013-5194
DOI
10.1049/el.2016.2951
URI
http://hdl.handle.net/10203/223299
Appears in Collection
GCT-Journal Papers(저널논문)
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