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 complete graph G=(V, E) where each edge is labeled+ or−, the CORRELATION
CLUSTERING problem asks to partition V into clusters to minimize the number of+ edges …

We consider the classic correlation clustering problem: Given a complete graph where
edges are labelled either+ or−, the goal is to find a partition of the vertices that minimizes the …

We study correlation clustering in the streaming setting. This problem has been studied
extensively and numerous algorithms have been developed, most requiring multiple passes …

We study parallel algorithms for correlation clustering. Each pair among n objects is labeled
as either “similar” or “dissimilar”. The goal is to partition the objects into arbitrarily many …

We present a new approach for solving (minimum disagreement) correlation clustering that
results in sublinear algorithms with highly efficient time and space complexity for this …

Grouping together similar elements in datasets is a common task in data mining and
machine learning. In this paper, we study streaming and parallel algorithms for correlation …

In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla ‍
(FOCS 2002), the input is a complete graph where edges are labeled either+ or−, and the …

In the correlation clustering problem the input is a signed graph where the sign indicates
whether each pair of points should be placed in the same cluster or not. The goal of the …

Correlation clustering is a fundamental optimization problem at the intersection of machine
learning and theoretical computer science. Motivated by applications to big data processing …