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  • 标题:Temporal Hierarchical Clustering
  • 本地全文:下载
  • 作者:Tamal K. Dey ; Alfred Rossi ; Anastasios Sidiropoulos
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2017
  • 卷号:92
  • 页码:28:1-28:12
  • DOI:10.4230/LIPIcs.ISAAC.2017.28
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:We study hierarchical clusterings of metric spaces that change over time. This is a natural geo- metric primitive for the analysis of dynamic data sets. Specifically, we introduce and study the problem of finding a temporally coherent sequence of hierarchical clusterings from a sequence of unlabeled point sets. We encode the clustering objective by embedding each point set into an ultrametric space, which naturally induces a hierarchical clustering of the set of points. We enforce temporal coherence among the embeddings by finding correspondences between successive pairs of ultrametric spaces which exhibit small distortion in the Gromov-Hausdorff sense. We present both upper and lower bounds on the approximability of the resulting optimization problems.
  • 关键词:clustering; hierarchical clustering; multi-objective optimization; dynamic metric spaces; moving point sets; approximation algorithms
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