Motivated by the fact that distances between data points in many real-world clustering
instances are often based on heuristic measures, Bilu and Linial Proceedings of the …

We investigate the notion of stability proposed by Bilu and Linial. We obtain an exact
polynomial-time algorithm for γ-stable Max Cut instances with for some absolute constant c> …

Machine learning algorithms are increasingly used to enhance agricultural productivity cost-
effectively. A critical task in precision agriculture is locating a plant's root collar. This is …

It is well known that most of the common clustering objectives are NP-hard to optimize. In
practice, however, clustering is being routinely carried out. One approach for providing …

Two genres of heuristics that are frequently reported to perform much better on" real-world"
instances than in the worst case are greedy algorithms and local search algorithms. In this …

The $ k $-center problem is a canonical and long-studied facility location and clustering
problem with many applications in both its symmetric and asymmetric forms. Both versions of …

The k-center problem is a canonical and long-studied facility location and clustering problem
with many applications in both its symmetric and asymmetric forms. Both versions of the …

We consider efficient clustering algorithm under data clusterability assumptions with added
noise. In contrast with most literature on this topic that considers either the adversarial noise …

Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain expert to
cluster data points by interactively making pairwise" same-cluster" queries. However, it is …

Pairwise" same-cluster" queries are one of the most widely used forms of supervision in
semi-supervised clustering. However, it is impractical to ask human oracles to answer every …