Cluster Editing: Kernelization based on Edge Cuts release_5imegy7njrbfbm4thyw3lfcmzi

by Yixin Cao, Jianer Chen

Released as a article .

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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Type  article
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Date   2010-09-03
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arXiv  1008.4250v2
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