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Online Clique Clustering

机译:在线单击群集

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摘要

Abstract Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, where each cluster forms a clique in the graph, while optimizing some objective function. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The goal is to maintain a clustering with an objective value close to the optimal solution. For the variant where we want to maximize the number of edges in the clusters, we propose an online algorithm based on the doubling technique. It has an asymptotic competitive ratio at most 15.646 and a strict competitive ratio at most 22.641. We also show that no deterministic algorithm can have an asymptotic competitive ratio better than 6. For the variant where we want to minimize the number of edges between clusters, we show that the deterministic competitive ratio of the problem is n-ω(1)documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} egin{document}$$n-omega (1)$$end{document}, where n is the number of vertices in the graph.
机译:摘要Clique Clustering是将图形的顶点分成不相交的群集的问题,其中每个群集在图中形成一个Clique,同时优化一些客观函数。在在线群集中,输入图是一次给出一个顶点,并且不允许将先前群集在一起的任何顶点。目标是维护与接近最佳解决方案的客观值的聚类。对于我们希望最大化集群中边缘数量的变体,我们提出了一种基于倍增技术的在线算法。它具有最多为15.646的渐近竞争比率,最多是严格的竞争比例为22.641。我们还表明,没有比6更好的确定性算法可以具有比6更好的渐近竞争比率。对于我们想要最小化集群之间的边缘的数量,我们表明问题的确定性竞争比率是n-ω(1) DepositureClass [12pt] {minimal} usepackage {ammath} usepackage {keysym} usepackage {amsfonts} usepackage {amssymb} usepackage {amsbsy} usepackage {mathrsfs} usepackage {supmeek} setLength { oddsidemargin} { - 69pt} begin {document} $$ n- omega(1)$$ end {document},其中n是图表中的顶点数。

著录项

  • 来源
    《Algorithmica》 |2020年第4期|938-965|共28页
  • 作者单位

    Department of Computer Science and Engineering University of California at Riverside Riverside USA;

    Laboratoire d’informatique de Paris 6 LIP6 CNRS Sorbonne Université 75252 Paris France;

    Department of Computer Science and Media Technology Malmö University 205 06 Malmö Sweden;

    Department of Computer Science and Media Technology Malmö University 205 06 Malmö Sweden;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Online algorithms; Graphs; Cliques; Competitive analysis; Clustering;

    机译:在线算法;图形;派系;竞争分析;聚类;

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