文章基本信息

标题：Temporal Hierarchical Clustering
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作者：Tamal K. Dey ; Alfred Rossi ; Anastasios Sidiropoulos 等
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