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2011
Springer

Improved Approximation Algorithms for Bipartite Correlation Clustering

10 years 1 months ago
Improved Approximation Algorithms for Bipartite Correlation Clustering
In this work we study the problem of Bipartite Correlation Clustering (BCC), a natural bipartite counterpart of the well studied Correlation Clustering (CC) problem. Given a bipartite graph, the objective of BCC is to generate a set of vertex-disjoint bi-cliques (clusters) which minimizes the symmetric difference to it. The best known approximation algorithm for BCC due to Amit (2004) guarantees an 11-approximation ratio.4 In this paper we present two algorithms. The first is an improved 4approximation algorithm. However, like the previous approximation algorithm, it requires solving a large convex problem which becomes prohibitive even for modestly sized tasks. The second algorithm, and our main contribution, is a simple randomized combinatorial algorithm. It also achieves an expected 4-approximation factor, it is trivial to implement and highly scalable. The analysis extends a method developed by Ailon, Charikar and Newman in 2008, where a randomized pivoting algorithm was analyzed...
Nir Ailon, Noa Avigdor-Elgrabli, Edo Liberty, Anke
Added 20 Dec 2011
Updated 20 Dec 2011
Type Journal
Year 2011
Where ESA
Authors Nir Ailon, Noa Avigdor-Elgrabli, Edo Liberty, Anke van Zuylen
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