Highly-Efficient Large-Scale k-means with Individual Fairness
Summary: Introduces tilted-SSE k-means via exponential tilting to bias centroids away from large assignment distances, targeting individual fairness in clustering/facility-location settings. TKM/FastTKM optimize this objective with CD/SGD and stochastic dynamics, giving Lloyd-like complexity but much better fairness/utility and large speedups. (summarized by gpt-5.4-mini on Apr 12 2026)
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Authors
- 1. Shengkun Zhu
- 2. Jinshan Zeng
- 3. Yuan Sun
- 4. Sheng Wang
- 5. Yiming Wang
- 6. Yushuai Ji
- 7. Feiping Nie
- 8. Xiaodong Li
- 9. Zhiyong Peng
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,327 | Big Metadata: When Metadata is Big Data | 2021 | VLDB | 6.2765351e-05 |
| 5,563 | Big Graphs: Challenges and Opportunities | 2022 | VLDB | 5.4328211e-05 |
| 6,745 | On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection | 2021 | VLDB | 4.9376468e-05 |
| 6,906 | Models and Mechanisms for Spatial Data Fairness | 2023 | VLDB | 4.8878659e-05 |
| 11,221 | F3 KM: Federated, Fair, and Fast k-means | 2023 | SIGMOD | 4.1905499e-05 |
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