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 …

Clustering is a classic topic in optimization with k-means being one of the most fundamental
such problems. In the absence of any restrictions on the input, the best-known algorithm for k …

What does it mean for a clustering to be fair? One popular approach seeks to ensure that
each cluster contains groups in (roughly) the same proportion in which they exist in the …

Clustering problems have numerous applications and are becoming more challenging as
the size of the data increases. In this paper, we consider designing clustering algorithms that …

We study the problem of k-means clustering in the presence of outliers. The goal is to cluster
a set of data points to minimize the variance of the points assigned to the same cluster, with …

The most well-known and ubiquitous clustering problem encountered in nearly every branch
of science is undoubtedly k-means: given a set of data points and a parameter k, select k …

We give a local search based algorithm for $ k $-median and $ k $-means (and more
generally for any $ k $-clustering with $\ell_p $ norm cost function) from the perspective of …

Aiming to unify known results about clustering mixtures of distributions under separation
conditions, Kumar and Kannan [1] introduced a deterministic condition for clustering …

The Uncapacitated Facility Location (UFL) problem is one of the most fundamental
clustering problems: Given a set of clients C and a set of facilities F in a metric space (C∪ F …

Abstract k-Means is a well-studied clustering problem that finds applications in many fields
related to unsupervised learning. It is known that k-means clustering is highly sensitive to the …