Approximate greedy clustering and distance selection for graph metrics
In this paper, we consider two important problems defined on finite metric spaces, and provide efficient new algorithms and approximation schemes for these problems on inputs given as graph shortest path metrics or high-dimensional Euclidean metrics. The first of these problems is the greedy permut...
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| Auteurs principaux: | , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Carleton University
2020-12-01
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| Collection: | Journal of Computational Geometry |
| Accès en ligne: | https://jocg.org/index.php/jocg/article/view/3115 |
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