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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| Main Authors: | , , |
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| Format: | Book Series |
| Published: |
2017
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| Online Access: | 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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| Institution: | Chiang Mai University |
| Summary: | © 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. |
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