The algorithms community has been modeling the underlying key features and constraints of
massively parallel frameworks and using these models to discover new algorithmic …

Given a set X of n points in a metric space, the problem of diversity maximization is to extract
a set S of k points from X so that the diversity of S is maximized. This problem is essential in …

We consider $ k $-means clustering of $ n $ data points in Euclidean space in the Massively
Parallel Computation (MPC) model, a computational model which is an abstraction of …

Clustering multi-dimensional points is a fundamental task in many fields, and density-based
clustering supports many applications as it can discover clusters of arbitrary shapes. This …

Center-based clustering techniques are fundamental to many real-world applications such
as data summarization and social network analysis. In this work, we study the problem of …

As a representative model for privacy preserving data publishing, K-anonymity has raised a
considerable number of questions for researchers over the past few decades. Among them …

We study the problem of $ k $-center clustering with outliers in arbitrary metrics and
Euclidean space. Though a number of methods have been developed in the past decades, it …

Clustering is a fundamental primitive in manifold applications. In order to achieve valuable
results, parameters of the clustering algorithm, eg, the number of clusters, have to be set …

In this paper we give the first efficient algorithms for the k-center problem on dynamic graphs
undergoing edge updates. In this problem, the goal is to partition the input into k sets by …

Given a point set P⊆ X of size n in a metric space (X, dist) of doubling dimension d and two
parameters k∈ ℕ and z∈ ℕ, the k-center problem with z outliers asks to return a set …