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Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance

arxiv.org/abs/2609.11173

Updated 16 min ago · first seen 11 Sept 2026

paper_01M294FP317143AYAC3NYYVGY8

Published
11 Sept 2026
T1 · 16 min ago
arXiv
2609.11173
T1 · 16 min ago
Category
cs.LG
T1 · 16 min ago

Abstract

Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster. Kleinberg's Impossibility Theorem formalizes this difficulty by showing that no flat clustering method can simultaneously satisfy three natural axioms: scale invariance, richness, and consistency. In this paper, we ask whether this impossibility persists when the output is a hierarchy rather than a single partition. We show that, in contrast to the flat clustering setting, the hierarchical analog of these axioms are jointly satisfiable. In fact, there exist uncountably many hierarchical clustering methods satisfying these axioms, which we call admissible. We explicitly construct several admissible methods, including methods based on well-separated clusters and a non-binary version of single linkage. For certain pairs of admissible methods, the hierarchy produced by one always refines that produced by the other. This refinement relation defines a partial order on the class of admissible methods. This partially ordered set has no greatest element and contains uncountably many pairwise incompatible maximal elements, revealing substantial diversity among admissible methods. Nevertheless, this diversity is constrained: every admissible method contains a hierarchy of sufficiently well-separated clusters, and every finite collection of admissible methods shares such a nontrivial common backbone.

Authors 4

Daichi Kuroda, Maximilien Dreveton, Matthias Grossglauser, Patrick Thiran

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

Arxiv announce type
new

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

arXiv id
2609.11173

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

Categories
cs.LG, stat.ME, stat.ML

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

Primary category
cs.LG

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 16 min agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

16 min ago

Conflicts

None