Title:	LVCluster: Bounded Clustering using Laguerre Voronoi Diagram
Authors:	Ohi, Abu Quwsar Gavrilova, Marina
Citation:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 255-262.
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/57397
ISSN:	2464–4625 (online) 2464–4617 (print)
Keywords:	Laguerrova geometrie;Voronoiův diagram;shlukování;KMeans;klesající gradient
Keywords in different language:	Laguerre Geometry;Voronoi Diagram;clustering;KMeans;gradient descending
Abstract in different language:	Clustering, 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2024: Full Papers Proceedings

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