Research map / Learning theory and algorithms / Clustering
Fair k-Center Clustering for Data Summarization
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Enforce per-group quotas and use recursive greedy center selection with group-aware swaps | k-center data summarization · Fair k-center, two groups | approximation factor: 5 | Helped |
| Use the recursive fair k-center algorithm for multiple demographic groups | k-center data summarization · Fair k-center, m groups | approximation factor: $(3\cdot 2^{m-1}-1)$ | Mixed |
| Use the proposed algorithm instead of matroid intersection | Fair k-center · Synthetic fair k-center, |S|=250 | running time: 230 seconds vs 0.01 seconds | Helped |
| Use the proposed algorithm rather than the matroid-center algorithm | Fair k-center · Synthetic fair k-center, seven settings, |S|=25 | approximation factor: never larger than 2.2 | Helped |
| Scale the number of groups across the tested range | Fair k-center algorithm · Synthetic fair k-center, m in {2,...,20} | approximation factor: never larger than 2.6 | No clear effect |
| Use either linear-time baseline heuristic instead of the proposed algorithm | Fair k-center · Synthetic and Adult data sets | clustering cost | No clear effect |
| Enforce balanced group quotas rather than use unconstrained greedy k-center | Data summarization · Synthetic and Adult data sets | clustering cost | Mixed |
| Use unconstrained greedy k-center without group quotas | Data summarization · Medical-doctor image summary | group representation: three or even four females | Hurt |
Closest papers by meaning
- Fair and Fast k-Center Clustering for Data Summarization (ICML 2022 · similarity 0.94)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.89)
- Efficient Constrained K-center Clustering with Background Knowledge (AAAI 2024 · similarity 0.89)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.88)
- Parameterized Approximation Schemes for Fair-Range Clustering (NeurIPS 2024 · similarity 0.88)
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.