DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Jeongmin | ko |
dc.contributor.author | Woo, Woon-Tack | ko |
dc.date.accessioned | 2017-04-17T07:29:47Z | - |
dc.date.available | 2017-04-17T07:29:47Z | - |
dc.date.created | 2017-04-10 | - |
dc.date.created | 2017-04-10 | - |
dc.date.created | 2017-04-10 | - |
dc.date.issued | 2017-02 | - |
dc.identifier.citation | ELECTRONICS LETTERS, v.53, no.3 | - |
dc.identifier.issn | 0013-5194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/223299 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.title | Divide-conquer method for improving possibilistic c-means | - |
dc.type | Article | - |
dc.identifier.wosid | 000395526800017 | - |
dc.identifier.scopusid | 2-s2.0-85011931149 | - |
dc.type.rims | ART | - |
dc.citation.volume | 53 | - |
dc.citation.issue | 3 | - |
dc.citation.publicationname | ELECTRONICS LETTERS | - |
dc.identifier.doi | 10.1049/el.2016.2951 | - |
dc.contributor.localauthor | Woo, Woon-Tack | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | divide and conquer methods | - |
dc.subject.keywordAuthor | fuzzy set theory | - |
dc.subject.keywordAuthor | pattern clustering | - |
dc.subject.keywordAuthor | divide-conquer method | - |
dc.subject.keywordAuthor | possibilistic c-means | - |
dc.subject.keywordAuthor | cluster centres | - |
dc.subject.keywordAuthor | coincident clusters | - |
dc.subject.keywordAuthor | close centres | - |
dc.subject.keywordAuthor | preclustered data information | - |
dc.subject.keywordAuthor | unclustered data information | - |
dc.subject.keywordAuthor | simulated magnetic resonance brain image data | - |
dc.subject.keywordAuthor | bias-field | - |
dc.subject.keywordAuthor | PCM clustering methods | - |
dc.subject.keywordAuthor | noise sensitivity | - |
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