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 …

Parameterization and approximation are two popular ways of coping with NP-hard
problems. More recently, the two have also been combined to derive many interesting …

We study the complexity of the classic capacitated k-median and k-means problems
parameterized by the number of centers, k. These problems are notoriously difficult since the …

In this article, we study the uniform capacitated k-median (CKM) problem. In the problem, we
are given a set F of potential facility locations, a set C of clients, a metric d over F∪ C, an …

There has been a recent surge of interest in incorporating fairness aspects into classical
clustering problems. Two recently introduced variants of the k-Center problem in this spirit …

Mobile applications becomes increasingly computation intensive, while the computing
capability of portable mobile devices is limited. Although cloud computing is emerging as a …

Capacitated k-median is one of the few outstanding optimization problems for which the
existence of a polynomial time constant factor approximation algorithm remains an open …

We study the Capacitated k-Median problem for which existing constant-factor
approximation algorithms are all pseudo-approximations that violate either the capacities or …

There has been a recent surge of interest in incorporating fairness aspects into classical
clustering problems. Two recently introduced variants of the k-Center problem in this spirit …

We study the ℱ-center problem with outliers: Given a metric space (X, d), a general down-
closed family ℱ of subsets of X, and a parameter m, we need to locate a subset S∈ ℱ of …