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dc.contributor.authorThierry Denœuxen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorOrakanya Kanjanatarakulen_US
dc.description.abstract© 2016 Elsevier B.V. In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by Dempster-Shafer 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 present several improvements to EVCLUS, making it applicable to very large dissimilarity data. First, the gradient-based optimization procedure in the original EVCLUS algorithm is replaced by a much faster iterative row-wise quadratic programming method. Secondly, we show that EVCLUS can be provided with only a random sample of the dissimilarities, reducing the time and space complexity from quadratic to roughly linear. Finally, we introduce a two-step approach to construct credal partitions assigning masses to selected pairs of clusters, making the algorithm outputs more informative than those of the original EVCLUS, while remaining manageable for large numbers of clusters.en_US
dc.subjectBusiness, Management and Accountingen_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.titleEvidential clustering of large dissimilarity dataen_US
article.title.sourcetitleKnowledge-Based Systemsen_US
article.volume106en_US de Technologie de Compiegneen_US Mai Universityen_US Mai Rajabhat Universityen_US
Appears in Collections:CMUL: Journal Articles

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