Cluster Editing: Kernelization based on Edge Cuts
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by
Yixin Cao, Jianer Chen
2010
Abstract
Kernelization algorithms for the cluster editing problem have been a
popular topic in the recent research in parameterized computation. Thus far
most kernelization algorithms for this problem are based on the concept of
critical cliques. In this paper, we present new observations and new
techniques for the study of kernelization algorithms for the cluster
editing problem. Our techniques are based on the study of the relationship
between cluster editing and graph edge-cuts. As an application, we
present an O(n^2)-time algorithm that constructs a 2k kernel for the
weighted version of the cluster editing problem. Our result meets
the best kernel size for the unweighted version for the cluster editing
problem, and significantly improves the previous best kernel of quadratic size
for the weighted version of the problem.
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