An overview of fairness in clustering
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …
feature ubiquitously in modern data science, and play a key role in many learning-based …
A review of clustering models in educational data science toward fairness-aware learning
Ensuring fair access to quality education is essential for every education system to fully
realize every student's potential. Nowadays, machine learning (ML) is transforming …
realize every student's potential. Nowadays, machine learning (ML) is transforming …
A trade-off algorithm for solving p-center problems with a graph convolutional network
The spatial optimization method between combinatorial optimization problems and GIS has
many geographical applications. The p-center problem is a classic NP-hard location …
many geographical applications. The p-center problem is a classic NP-hard location …
Constant approximation for individual preference stable clustering
Individual preference (IP) stability, introduced by Ahmadi et al.(ICML 2022), is a natural
clustering objective inspired by stability and fairness constraints. A clustering is $\alpha $-IP …
clustering objective inspired by stability and fairness constraints. A clustering is $\alpha $-IP …
Balanced Fair K-Means Clustering
R Pan, C Zhong, J Qian - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Fairness in clustering has recently received significant attention. The goal of fair clustering is
to ensure that a clustering algorithm mitigates or even eliminates bias in the original dataset …
to ensure that a clustering algorithm mitigates or even eliminates bias in the original dataset …
Doubly constrained fair clustering
J Dickerson, S Esmaeili… - Advances in Neural …, 2024 - proceedings.neurips.cc
The remarkable attention which fair clustering has received in the last few years has resulted
in a significant number of different notions of fairness. Despite the fact that these notions are …
in a significant number of different notions of fairness. Despite the fact that these notions are …
Constant-Factor Approximation Algorithms for Socially Fair -Clustering
We study approximation algorithms for the socially fair $(\ell_p, k) $-clustering problem with
$ m $ groups, whose special cases include the socially fair $ k $-median ($ p= 1$) and …
$ m $ groups, whose special cases include the socially fair $ k $-median ($ p= 1$) and …
Efficient algorithms for fair clustering with a new notion of fairness
We revisit the problem of fair clustering, first introduced by Chierichetti et al.(Fair clustering
through fairlets, 2017), which requires each protected attribute to have approximately equal …
through fairlets, 2017), which requires each protected attribute to have approximately equal …
Scalable Algorithms for Individual Preference Stable Clustering
R Mosenzon, A Vakilian - International Conference on …, 2024 - proceedings.mlr.press
In this paper, we study the individual preference (IP) stability, which is an notion capturing
individual fairness and stability in clustering. Within this setting, a clustering is $\alpha $-IP …
individual fairness and stability in clustering. Within this setting, a clustering is $\alpha $-IP …
Feature-based individual fairness in k-clustering
Ensuring fairness in machine learning algorithms is a challenging and essential task. We
consider the problem of clustering a set of points while satisfying fairness constraints. While …
consider the problem of clustering a set of points while satisfying fairness constraints. While …