Generalized K-means for Metric Space Clustering Using PageRank

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dc.contributor.authorHajij, Mustafaen_US
dc.contributor.authorSaid, Eyaden_US
dc.contributor.authorTodd, Roberten_US
dc.contributor.editorRitsos, Panagiotis D. and Xu, Kaien_US
dc.date.accessioned2020-09-10T06:27:51Z
dc.date.available2020-09-10T06:27:51Z
dc.date.issued2020
dc.description.abstractWe utilize the PageRank vector to generalize the k-means clustering algorithm to directed and undirected graphs. We demonstrate that PageRank and other centrality measures can be used in our setting to robustly compute centrality of nodes in a given graph. Furthermore, we show how our method can be generalized to metric spaces and apply it to other domains such as point clouds and triangulated meshes.en_US
dc.description.sectionheadersGraphics
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20201152
dc.identifier.isbn978-3-03868-122-9
dc.identifier.pages63-66
dc.identifier.urihttps://doi.org/10.2312/cgvc.20201152
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20201152
dc.publisherThe Eurographics Associationen_US
dc.titleGeneralized K-means for Metric Space Clustering Using PageRanken_US
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Computer Graphics & Visual Computing (CGVC) 2020