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 …

This paper studies the fair range clustering problem in which the data points are from
different demographic groups and the goal is to pick $ k $ centers with the minimum …

We introduce a novel definition for a small set R of k points being" representative" of a larger
set in a metric space. Given a set V (eg, documents or voters) to represent, and a set C of …

Class imbalance and group (eg, race, gender, and age) imbalance are acknowledged as
two reasons in data that hinder the trade-off between fairness and utility of machine learning …

We present a scalable algorithm for the individually fair ($ p $, $ k $)-clustering problem
introduced by Jung et al. and Mahabadi et al. Given $ n $ points $ P $ in a metric space, let …

We study the proportional clustering problem of Chen et al.(ICML'19) and relate it to the area
of multiwinner voting in computational social choice. We show that any clustering satisfying …

While virtual learning environments (VLEs) are widely used in K-12 education for classroom
instruction and self-study, young students' success in VLEs highly depends on their self …

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 …

Many clustering algorithms are guided by certain cost functions such as the widely-used k-
means cost. These algorithms divide data points into clusters with often complicated …

In this paper, we propose a natural notion of individual preference (IP) stability for clustering,
which asks that every data point, on average, is closer to the points in its own cluster than to …