In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …

In this paper, we study the problem of fair clustering on the $ k-$ center objective. In fair
clustering, the input is $ N $ points, each belonging to at least one of $ l $ protected groups …

We study the problem of finding low-cost {\em fair clusterings} in data where each data point
may belong to many protected groups. Our work significantly generalizes the seminal work …

We study the question of fair clustering under the {\em disparate impact} doctrine, where
each protected class must have approximately equal representation in every cluster. We …

We study data clustering problems with $\ell_p $-norm objectives (eg\textsc {$ k $-Median}
and\textsc {$ k $-Means}) in the context of individual fairness. The dataset consists of $ n …

We study fair clustering problems as proposed by Chierichetti et al.(NIPS 2017). Here, points
have a sensitive attribute and all clusters in the solution are required to be balanced with …

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned
into clusters that consist of nearby points in a metric space. A recent variant, fair clustering …

Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and
may be used to make decisions for each point based on its group. However, this process …

We study the fair variant of the classic k-median problem introduced by (Chierichetti et al.,
NeurIPS 2017) in which the points are colored, and the goal is to minimize the same …

A clustering may be considered as fair on pre-specified sensitive attributes if the proportions
of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we …