# Improved Approximation Algorithms for Individually Fair Clustering

@article{Vakilian2022ImprovedAA, title={Improved Approximation Algorithms for Individually Fair Clustering}, author={Ali Vakilian and Mustafa Yalçiner}, journal={ArXiv}, year={2022}, volume={abs/2106.14043} }

We consider the k -clustering problem with (cid:96) p -norm cost, which includes k -median, k means and k -center, under an individual notion of fairness proposed by Jung et al. [2020]: given a set of points P of size n , a set of k centers induces a fair clustering if every point in P has a center among its n/k closest neighbors. Mahabadi and Vakilian [2020] presented a ( p O ( p ) , 7)-bicriteria approximation for fair clustering with (cid:96) p norm cost: every point ﬁnds a center within…

## 16 Citations

### An Overview of Fairness in Clustering

- Computer ScienceIEEE Access
- 2021

This survey aims to provide researchers with an organized overview of the field, and motivate new and unexplored lines of research regarding fairness in clustering, and to bridge the gap by categorizing existing research on fair clustering.

### Taxonomy of Fairness Concepts in Clustering

- Computer Science
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All different concepts of demographic fairness that have been studied in the context of clustering are presented.

### Bicriteria Approximation Algorithms for Priority Matroid Median

- MathematicsArXiv
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Fairness considerations have motivated new clustering problems and algorithms in recent years. In this paper we consider the Priority Matroid Median problem which generalizes the Priority k -Median…

### FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

- Computer ScienceArXiv
- 2022

A novel active learning strategy called Fair Active Learning using fair Clustering, Uncertainty, and Representativeness (FAL-CUR) that provides a high accuracy while maintaining fairness during the sample acquisition phase and outperforms state-of-the-art methods on well-known fair active learning problems.

### Modiﬁcation-Fair Cluster Editing

- Computer Science
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A modiﬁcation fairness constraint is proposed which ensures that the number of edits incident to each subgroup is proportional to its size, and results show that the problem is NP-hard even if one may only insert edges within a subgroup.

### Individual Preference Stability for Clustering

- Computer ScienceICML
- 2022

It is shown that deciding whether a given data set allows for an IP-stable clustering in general is NP-hard, and the design of efﬁcient algorithms for ef-stable clusterings in some restricted metric spaces are explored.

### Approximation Algorithms for Continuous Clustering and Facility Location Problems

- Computer ScienceESA
- 2022

It is shown that, for the continuous versions of some clustering problems, one can design approximation algorithms attaining a better factor than the β -factor blow-up mentioned above, and this technique based on the round-or-cut framework is described.

### Constant-Factor Approximation Algorithms for Socially Fair k-Clustering

- Computer ScienceArXiv
- 2022

The performance of these algorithms are compared with existing bicriteria algorithms as well as exactly k center approximation algorithms on benchmark datasets, and it is found that these algorithms outperform existing methods in practice.

### Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina

- EconomicsArXiv
- 2022

Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over the decades. A…

### On Coresets for Fair Regression and Individually Fair Clustering

- Computer ScienceAISTATS
- 2022

This paper defines coresets for Fair Regression with Statistical Parity (SP) constraints and for Individually Fair Clustering and shows that to obtain such coresets, it is sufficient to sample points based on the probabilities dependent on combination of sensitivity score and a carefully chosen term according to the fairness constraints.

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