I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies

• URL: http://arxiv.org/abs/2502.14018v1
• Date: Wed, 19 Feb 2025 18:03:52 GMT
• Title: I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies
• Authors: Andrew Draganov, Pascal Weber, Rasmus Skibdahl Melanchton Jørgensen, Anna Beer, Claudia Plant, Ira Assent,
• Abstract summary: We show that, for any reasonable hierarchy, one can optimally solve any center-based clustering objective over it. We conclude by verifying the utility of our proposed techniques across datasets, hierarchies, and partitioning schemes.
• Score: 11.69714244591334
• License:
• Abstract: Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen. This paper generalizes these ideas by proving that, for any reasonable hierarchy, one can optimally solve any center-based clustering objective over it (such as $k$-means). Moreover, these solutions can be found exceedingly quickly and are themselves necessarily hierarchical. Thus, given a cluster tree, we show that one can quickly access a plethora of new, equally meaningful hierarchies. Just as in standard hierarchical clustering, one can then choose any desired partition from these new hierarchies. We conclude by verifying the utility of our proposed techniques across datasets, hierarchies, and partitioning schemes.

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