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 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 …

The $ k $-center problem is a classical combinatorial optimization problem which asks to
find $ k $ centers such that the maximum distance of any input point in a set $ P $ to its …

In fully dynamic clustering problems, a clustering of a given data set in a metric space must
be maintained while it is modified through insertions and deletions of individual points. In …

Clustering problems are well studied in a variety of fields, such as data science, operations
research, and computer science. Such problems include variants of center location …

In this work, we study a range of constrained versions of the k-supplier and k-center
problems. In the classical (unconstrained) k-supplier problem, we are given a set of clients C …

Clustering of data in metric spaces is a fundamental problem and has many applications in
data mining and it is often used as an unsupervised learning tool inside other machine …

The Metric $ k $-median problem over a metric space $(\mathcal {X}, d) $ is defined as
follows: given a set $ L\subseteq\mathcal {X} $ of facility locations and a set …

Many manifold embedding algorithms fail apparently when the data manifold has a large
aspect ratio (such as a long, thin strip). Here, we formulate success and failure in terms of …

The problem of constrained clustering has attracted significant attention in the past decades.
In this paper, we study the balanced k-center, k-median, and k-means clustering problems …