Research map / Learning theory and algorithms / Clustering
Fair clustering approximation
Approximation algorithms for centroid clustering with demographic fairness, such as fairlets.
31 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2026) · an approach in Clustering, keyword cluster “center · median · coreset”
State History Tried Checklist Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
4 papers in 2023–24 and 14 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 2.9% to 6.8%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2017 · Fair Clustering Through Fairlets (NeurIPS 2017 · 1 citation)
- 2019 · Fair k-Center Clustering for Data Summarization (ICML 2019 · 75 citations)
- 2020 · Individual Fairness for k-Clustering (ICML 2020 · 31 citations)
- 2021 · Better Algorithms for Individually Fair $k$-Clustering (NeurIPS 2021 · 5 citations)
- 2022 · Fair and Fast k-Center Clustering for Data Summarization (ICML 2022)
- 2023 · Approximation Algorithms for Fair Range Clustering (ICML 2023 · 4 citations)
- 2024 · Towards a Theoretical Understanding of Why Local Search Works for Clustering with Fair-Center Representation (AAAI 2024 · 3 citations)
- 2025 · Relative Error Fair Clustering in the Weak-Strong Oracle Model (ICML 2025 · 0 citations)
- 2026 · Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms (ICML 2026 · 0 citations)
Earliest papers here
- Fair Clustering Through Fairlets (NeurIPS 2017 · 1 citation)
- Fair k-Center Clustering for Data Summarization (ICML 2019 · 75 citations)
- Fair Algorithms for Clustering (NeurIPS 2019 · 59 citations)
What has been tried
20 papers here report 151 measured changes (a component added, removed or swapped, compared with a baseline): helped 66%, no clear effect 8%, hurt 23%, mixed 3%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Sample the relaxed candidate set T at 50% | Fair k-means benchmarks · clustering cost | no more than 10% | Hurt | Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · 0 citations) |
What reviewers will expect
Datasets and baselines named in the 6 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.
Datasets
- Adult · 3 of 6 papers
- Bank · 2 of 6 papers
- Census · 2 of 6 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 18 papers; from OpenAlex and the papers' affiliations).
- Central South University · 2 papers
Use it
Download BibTeX of 30 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Coresets and dynamic clustering (77 papers)
- k-means++ seeding analysis (76 papers)
Elsewhere in the topic
- Clustering approximation algorithms (150 papers)
- Incomplete multi-view clustering (125 papers)
- Correlation clustering (102 papers)
- Subspace clustering (102 papers)
- Spectral clustering algorithms (43 papers)
- Fairness-constrained clustering (19 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).