# Cluster-level Group Representativity Fairness in k-means Clustering

arXiv: https://arxiv.org/abs/2212.14467 · HTML: https://mlsanity.com/paper/2212.14467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [Fair clustering via equitable group representations](https://mlsanity.com/paper/2006.11009/) (2020)
- [A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set](https://mlsanity.com/paper/2003.14263/) (2020)
- [Improved Approximation Algorithms for Individually Fair Clustering](https://mlsanity.com/paper/2106.14043/) (2021)
- [On the Moral Justification of Statistical Parity](https://mlsanity.com/paper/2011.02079/) (2020)
- [Probabilistic Fair Clustering](https://mlsanity.com/paper/2006.10916/) (2020)
- [A Pairwise Fair and Community-preserving Approach to k-Center Clustering](https://mlsanity.com/paper/2007.07384/) (2020)
- [Fair Hierarchical Clustering](https://mlsanity.com/paper/2006.10221/) (2020)
- [Fairness in Clustering with Multiple Sensitive Attributes](https://mlsanity.com/paper/1910.05113/) (2019)
- [A New Notion of Individually Fair Clustering: α-Equitable k-Center](https://mlsanity.com/paper/2106.05423/) (2021)
- [Representativity Fairness in Clustering](https://mlsanity.com/paper/2010.07054/) (2020)
- [Whither Fair Clustering?](https://mlsanity.com/paper/2007.07838/) (2020)

## Abstract (arXiv)

There has been much interest recently in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and gender. We observe that clustering algorithms could generate clusters such that different groups are disadvantaged within different clusters. We develop a clustering algorithm, building upon the centroid clustering paradigm pioneered by classical algorithms such as k-means, where we focus on mitigating the unfairness experienced by the most-disadvantaged group within each cluster. Our method uses an iterative optimisation paradigm whereby an initial cluster assignment is modified by reassigning objects to clusters such that the worst-off sensitive group within each cluster is benefitted. We demonstrate the effectiveness of our method through extensive empirical evaluations over a novel evaluation metric on real-world datasets. Specifically, we show that our method is effective in enhancing cluster-level group representativity fairness significantly at low impact on cluster coherence.