Generalized K-means for Metric Space Clustering Using PageRank

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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

        
@inproceedings{
10.2312:cgvc.20201152
, booktitle = {
Computer Graphics and Visual Computing (CGVC)
}, editor = {
Ritsos, Panagiotis D. and Xu, Kai
}, title = {{
Generalized K-means for Metric Space Clustering Using PageRank
}}, author = {
Hajij, Mustafa
and
Said, Eyad
and
Todd, Robert
}, year = {
2020
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-122-9
}, DOI = {
10.2312/cgvc.20201152
} }
Citation