From Logits to Hierarchies: Hierarchical Clustering made Simple
Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt

TL;DR
This paper introduces a simple, scalable hierarchical clustering method built on pre-trained non-hierarchical models, outperforming complex deep models and applicable in supervised settings without fine-tuning.
Contribution
The paper presents a lightweight hierarchical clustering approach that leverages pre-trained logits, offering a practical alternative to complex deep hierarchical models with broad applicability.
Findings
Outperforms deep hierarchical clustering models in scalability and performance.
Applicable to pre-trained models without fine-tuning.
Effective in both unsupervised and supervised hierarchical tasks.
Abstract
The hierarchical structure inherent in many real-world datasets makes the modeling of such hierarchies a crucial objective in both unsupervised and supervised machine learning. While recent advancements have introduced deep architectures specifically designed for hierarchical clustering, we adopt a critical perspective on this line of research. Our findings reveal that these methods face significant limitations in scalability and performance when applied to realistic datasets. Given these findings, we present an alternative approach and introduce a lightweight method that builds on pre-trained non-hierarchical clustering models. Remarkably, our approach outperforms specialized deep models for hierarchical clustering, and it is broadly applicable to any pre-trained clustering model that outputs logits, without requiring any fine-tuning. To highlight the generality of our approach, we…
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Taxonomy
TopicsAdvanced Clustering Algorithms Research
