A straightforward natural iterative heuristic for correlation clustering in the general setting is to start from singleton clusters and whenever merging two clusters improves the current quality score merge them into a single cluster. We analyze the approximation and complexity aspects of this heuristic and its randomized variant where two clusters to merge are chosen uniformly at random among cluster pairs amenable to merge
We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) f...
We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) f...
Correlation clustering, or multicut partitioning, is widely used in image segmentation for partition...
Abstract. Correlation clustering is the problem of finding a crisp par-tition of the vertices of a c...
Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation ...
AbstractThe Correlation Clustering problem has been introduced recently [N. Bansal, A. Blum, S. Chaw...
A clustering algorithm is described which is powerful, in that at each iterative step of the method ...
Correlation clustering is a widely studied framework for clustering based on pairwise similarity and...
This paper introduces a polynomial time approxima-tion scheme for the metric Correlation Cluster-ing...
In Overlapping Correlation Clustering (OCC), a number of objects are assigned to clusters. Two objec...
Given a complete graph $G = (V, E)$ where each edge is labeled $+$ or $-$, the Correlation Clusterin...
AbstractWe consider the following general correlation-clustering problem [N. Bansal, A. Blum, S. Cha...
We continue the investigation of problems concerning correlation clustering or clustering with quali...
We continue the investigation of problems concerning correlation clustering or clustering with quali...
Correlation clustering aims at grouping the data set into correlation clusters such that the objects...
We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) f...
We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) f...
Correlation clustering, or multicut partitioning, is widely used in image segmentation for partition...
Abstract. Correlation clustering is the problem of finding a crisp par-tition of the vertices of a c...
Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation ...
AbstractThe Correlation Clustering problem has been introduced recently [N. Bansal, A. Blum, S. Chaw...
A clustering algorithm is described which is powerful, in that at each iterative step of the method ...
Correlation clustering is a widely studied framework for clustering based on pairwise similarity and...
This paper introduces a polynomial time approxima-tion scheme for the metric Correlation Cluster-ing...
In Overlapping Correlation Clustering (OCC), a number of objects are assigned to clusters. Two objec...
Given a complete graph $G = (V, E)$ where each edge is labeled $+$ or $-$, the Correlation Clusterin...
AbstractWe consider the following general correlation-clustering problem [N. Bansal, A. Blum, S. Cha...
We continue the investigation of problems concerning correlation clustering or clustering with quali...
We continue the investigation of problems concerning correlation clustering or clustering with quali...
Correlation clustering aims at grouping the data set into correlation clusters such that the objects...
We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) f...
We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) f...
Correlation clustering, or multicut partitioning, is widely used in image segmentation for partition...
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