2016
DOI: 10.1016/j.knosys.2016.05.043
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Evidential clustering of large dissimilarity data

Abstract: 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 optimizatio… Show more

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Cited by 73 publications
(90 citation statements)
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“…Three main algorithms have been proposed to generate credal partitions: the Evidential c-means (ECM) [15,16], EK-NNclus [8], and EVCLUS [9,10]. These algorithms are described in the next sections.…”
Section: Fuzzy and Hard Partitionsmentioning
confidence: 99%
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“…Three main algorithms have been proposed to generate credal partitions: the Evidential c-means (ECM) [15,16], EK-NNclus [8], and EVCLUS [9,10]. These algorithms are described in the next sections.…”
Section: Fuzzy and Hard Partitionsmentioning
confidence: 99%
“…The EVCLUS algorithm [9,10] applies some ideas from Multidimensional Scaling (MDS) [4] to clustering. Let D = (d ij ) be an n×n dissimilarity matrix, where d ij denotes the dissimilarity between objects o i and o j .…”
Section: Evclusmentioning
confidence: 99%
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