Research map / Learning theory and algorithms / Clustering
Coresets for Clustering in Graphs of Bounded Treewidth
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| importance-sampling coreset (Feldman-Langberg style, sensitivities from Thorup approx) instead of uniform sampling | graph k-MEDIAN coreset on NYC road network · X_man (Manhattan-concentrated), k=25 | empirical error (%): e.g. 4.87% vs 21.2% at size 1250 | Helped |
| importance-sampling coreset instead of uniform sampling | graph k-MEDIAN coreset on NYC road network · X_uni (uniform data), k=25 | empirical error (%): comparable; ours slightly better at some sizes (e.g. 4.57% vs 5.32% at 1250) and equal or worse at others (17.2% vs 17.2 | Mixed |
| coreset size increased from 25 to 19683 points | importance-sampling coreset · X_uni, k=25 | empirical error (%): 32.1% down to 1.27% | Helped |
| local search run on coreset D with centers restricted to D (D x D) instead of full graph local search on X | local search for graph k-MEDIAN · NYC road network | running time and objective: about 1000x faster at |D|≈1000 with 5%-10% error | Helped |
| local search run on coreset with centers from V (D x V) | local search for graph k-MEDIAN · NYC road network | running time: only moderate speedup | Helped |
Closest papers by meaning
- Coresets for Clustering Using Noisy Comparisons and Few Distance Queries (NeurIPS 2026 · similarity 0.90)
- Universal Weak Coreset (AAAI 2024 · similarity 0.88)
- Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling (NeurIPS 2026 · similarity 0.88)
- Coresets for Clustering Under Stochastic Noise (NeurIPS 2025 · similarity 0.88)
- Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability (ICML 2025 · similarity 0.87)
- Faster Approximation Algorithms for k-Center via Data Reduction (ICML 2025 · similarity 0.87)
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