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arXiv · 1707.09904

Temporal Hierarchical Clustering

Tamal K. Dey·Alfred Rossi·Anastasios Sidiropoulos
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Abstract

We study hierarchical clusterings of metric spaces that change over time. This is a natural geometric 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.

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Tamal K. Dey, Alfred Rossi, Anastasios Sidiropoulos. 2017-07-31. Temporal Hierarchical Clustering. https://arxiv.org/abs/1707.09904

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