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dc.contributor.authorMyhre, Jonas Nordhaug
dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorLøkse, Sigurd
dc.contributor.authorJenssen, Robert
dc.date.accessioned2016-03-09T14:54:05Z
dc.date.available2016-03-09T14:54:05Z
dc.date.issued2015-06-09
dc.description.abstractIn this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In combining these frameworks, two well known problematic issues are directly bypassed; the kernel bandwidth choice of the kernel density based mean shift and the computational complexity of the mean shift iterations. We demonstrate experiments on both real and synthetic data as a proof of concept for our contributions.en_US
dc.descriptionPublished version. Source at <a href=http://doi.org/10.1007/978-3-319-19665-7_15>http://doi.org/10.1007/978-3-319-19665-7_15</a>.en_US
dc.identifier.citationLecture Notes in Computer Science. 11 p. Springer, 2015en_US
dc.identifier.cristinIDFRIDAID 1317069
dc.identifier.doihttp://doi.org/10.1007/978-3-319-19665-7_15
dc.identifier.isbn978-3-319-19665-7
dc.identifier.urihttps://hdl.handle.net/10037/8818
dc.identifier.urnURN:NBN:no-uit_munin_8353
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rights.accessRightsopenAccess
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.titleConsensus Clustering Using kNN Mode Seekingen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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