Research map / Learning theory and algorithms / Clustering
Fair and Fast k-Center Clustering for Data Summarization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed linear-time fair k-center algorithm instead of matroid-center algorithm | fair k-center · Simulated graph data (|S|=25) | approximation factor | Helped |
| Use the proposed linear-time algorithm instead of the matroid-center algorithm (running time) | fair k-center · Running time vs |S| (m=5) | running time | Helped |
| Compare fair algorithm against simple per-group and greedy-with-quota heuristics | fair k-center · Adult and graph data | cost of solution | No clear effect |
| Enforce fairness constraint (proposed algorithm) vs unfair greedy k-center | fair k-center · Simulated data with varying m | cost (price of fairness) | No clear effect |
Closest papers by meaning
- Fair k-Center Clustering for Data Summarization (ICML 2019 · similarity 0.94)
- Scalable and Globally Optimal Generalized L₁ K-center Clustering via Constraint Generation in Mixed Integer Linear Programming (AAAI 2023 · similarity 0.89)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.88)
- Efficient Constrained K-center Clustering with Background Knowledge (AAAI 2024 · similarity 0.87)
- Dynamic k-center clustering with lifetimes (NeurIPS 2026 · similarity 0.87)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.87)
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