| dc.contributor.author | Hajij, Mustafa | en_US |
| dc.contributor.author | Said, Eyad | en_US |
| dc.contributor.author | Todd, Robert | en_US |
| dc.contributor.editor | Ritsos, Panagiotis D. and Xu, Kai | en_US |
| dc.date.accessioned | 2020-09-10T06:27:51Z | |
| dc.date.available | 2020-09-10T06:27:51Z | |
| dc.date.issued | 2020 | |
| dc.identifier.isbn | 978-3-03868-122-9 | |
| dc.identifier.uri | https://doi.org/10.2312/cgvc.20201152 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/cgvc20201152 | |
| dc.description.abstract | We 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.publisher | The Eurographics Association | en_US |
| dc.title | Generalized K-means for Metric Space Clustering Using PageRank | en_US |
| dc.description.seriesinformation | Computer Graphics and Visual Computing (CGVC) | |
| dc.description.sectionheaders | Graphics | |
| dc.identifier.doi | 10.2312/cgvc.20201152 | |
| dc.identifier.pages | 63-66 | |
