Research map / Learning theory and algorithms / Clustering
Parameterized Approximation Algorithms for K-center Clustering and Variants
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Voronoi separators to guess centers near the separator and recurse on separated subinstances | Euclidean k-center · Euclidean k-center, d dimensions | running time: 2^{O((k\log k)/{\epsilon})}dn -> O(|\mathcal C| \cdot k \cdot (1/\epsilon)^{O(d)} + 2^{O((1/\epsilon)^{O(d)} \cdot k^{1- | Helped |
| Extend the center-selection scheme to handle different prescribed radii using MEBs and farthest uncovered points | Non-uniform k-center · Euclidean non-uniform k-center | approximation factor: (1+\epsilon) | Helped |
| Use the closest available facility and open a ball with three times the guessed optimal radius | Non-uniform k-center · General metrics | approximation factor: 3 | Helped |
| Restrict the general-metric algorithm to client-centered facilities when \mathcal{C}=\mathcal{F} | Non-uniform k-center · General metrics, \mathcal{C}=\mathcal{F} | approximation factor: 3 -> 2 | Helped |
Closest papers by meaning
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