K-EVCLUS: Clustering large dissimilarity data in the belief function framework
© Springer International Publishing Switzerland 2016. In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimila...
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th-cmuir.6653943832-425482017-09-28T04:27:46Z K-EVCLUS: Clustering large dissimilarity data in the belief function framework Kanjanatarakul O. Sriboonchitta S. Denoeux T. © Springer International Publishing Switzerland 2016. In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we propose to replace the gradient-based optimization procedure in the original EVCLUS algorithm by a much faster iterative rowwise quadratic programming method. We also show that EVCLUS can be provided with only a random sample of the dissimilarities, reducing the time and space complexity from quadratic to linear. These improvements make EVCLUS suitable to cluster large dissimilarity datasets. 2017-09-28T04:27:46Z 2017-09-28T04:27:46Z 2016-01-01 Book Series 03029743 2-s2.0-84988629130 10.1007/978-3-319-45559-4_11 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84988629130&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42548 |
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© Springer International Publishing Switzerland 2016. In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we propose to replace the gradient-based optimization procedure in the original EVCLUS algorithm by a much faster iterative rowwise quadratic programming method. We also show that EVCLUS can be provided with only a random sample of the dissimilarities, reducing the time and space complexity from quadratic to linear. These improvements make EVCLUS suitable to cluster large dissimilarity datasets. |
| format |
Book Series |
| author |
Kanjanatarakul O. Sriboonchitta S. Denoeux T. |
| spellingShingle |
Kanjanatarakul O. Sriboonchitta S. Denoeux T. K-EVCLUS: Clustering large dissimilarity data in the belief function framework |
| author_facet |
Kanjanatarakul O. Sriboonchitta S. Denoeux T. |
| author_sort |
Kanjanatarakul O. |
| title |
K-EVCLUS: Clustering large dissimilarity data in the belief function framework |
| title_short |
K-EVCLUS: Clustering large dissimilarity data in the belief function framework |
| title_full |
K-EVCLUS: Clustering large dissimilarity data in the belief function framework |
| title_fullStr |
K-EVCLUS: Clustering large dissimilarity data in the belief function framework |
| title_full_unstemmed |
K-EVCLUS: Clustering large dissimilarity data in the belief function framework |
| title_sort |
k-evclus: clustering large dissimilarity data in the belief function framework |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84988629130&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42548 |
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1681422210321874944 |