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DC poleHodnotaJazyk
dc.contributor.authorOhi, Abu Quwsar
dc.contributor.authorGavrilova, Marina
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-29T18:28:01Z-
dc.date.available2024-07-29T18:28:01Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 255-262.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57397
dc.description.sponsorshipThe authors acknowledge the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant funding, as well as the NSERC Strategic Partnership Grant (SPG) and the University of Calgary Transdisciplinary Connector Funding for the partial funding of this project.cs_CZ
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectLaguerrova geometriecs
dc.subjectVoronoiův diagramcs
dc.subjectshlukovánícs
dc.subjectKMeanscs
dc.subjectklesající gradientcs
dc.titleLVCluster: Bounded Clustering using Laguerre Voronoi Diagramen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedClustering, a fundamental technique in unsupervised learning, identifies similar groups within a dataset. However, clustering algorithms encounter limitations when requiring a predetermined number of clusters/centroids/labels. This paper proposes a novel approach of clustering by integrating concepts from Voronoi diagrams in Laguerre geometry, namely, Laguerre Voronoi Clustering (LVCluster). Laguerre geometry introduces circles by adding radius weight metric to centroids, enabling dynamic exclusion from clustering criteria. Consequently, this approach offers flexibility by necessitating only one hyperparameter, an upper-bound value for the number of circles. LVCluster can be optimized using gradient descent and can be jointly optimized with deep neural network architectures. The experimental results indicated that LVCluster outperforms clustering algorithms when trained individually and jointly with deep neural networks on increased cluster centroids.en
dc.subject.translatedLaguerre Geometryen
dc.subject.translatedVoronoi Diagramen
dc.subject.translatedclusteringen
dc.subject.translatedKMeansen
dc.subject.translatedgradient descendingen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.26
dc.type.statusPeer revieweden
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