Generalized K-means for Metric Space Clustering Using PageRank
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2020Author
Hajij, Mustafa 
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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.
BibTeX
@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}
}
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}
}