Please use this identifier to cite or link to this item:
Title: K-EVCLUS: Clustering large dissimilarity data in the belief function framework
Authors: Orakanya Kanjanatarakul
Songsak Sriboonchitta
Thierry Denoeux
Keywords: Computer Science
Issue Date: 1-Jan-2016
Abstract: © 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.
ISSN: 16113349
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.