The correlation clustering problem identifies clusters in a set of objects when the qualitative
information about objects' mutual similarities or dissimilarities is given in a signed network …

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 …

In this paper, we study correlation clustering under fairness constraints. Fair variants of $ k $-
median and $ k $-center clustering have been studied recently, and approximation …

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

Correlation clustering is a central topic in unsupervised learning, with many applications in
ML and data mining. In correlation clustering, one receives as input a signed graph and the …

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 …

In this paper, we address the problem of\emphcorrelation clustering in the dynamic data
stream model. The stream consists of updates to the edge weights of a graph on n nodes …

Storing data in synthetic DNA offers the possibility of improving information density and
durability by several orders of magnitude compared to current storage technologies …

Given a similarity graph between items, correlation clustering (CC) groups similar items
together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster …