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arXiv · 2601.19718

Rethinking Divisive Hierarchical Clustering from a Distributional Perspective

Kaifeng Zhang·Kai Ming Ting·Tianrun Liang·Qiuran Zhao
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Abstract

We uncover that current objective-based Divisive Hierarchical Clustering (DHC) methods produce a dendrogram that does not have three desired properties i.e., no unwarranted splitting, group similar clusters into a same subset, ground-truth correspondence. This shortcoming has their root cause in using a set-oriented bisecting assessment criterion. We show that this shortcoming can be addressed by using a distributional kernel, instead of the set-oriented criterion; and the resultant clusters achieve a new distribution-oriented objective to maximize the total similarity of all clusters (TSC). Our theoretical analysis shows that the resultant dendrogram guarantees a lower bound of TSC. The empirical evaluation shows the effectiveness of our proposed method on artificial and Spatial Transcriptomics (bioinformatics) datasets. Our proposed method successfully creates a dendrogram that is consistent with the biological regions in a Spatial Transcriptomics dataset, whereas other contenders fail.

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Kaifeng Zhang, Kai Ming Ting, Tianrun Liang, Qiuran Zhao. 2026-01-27. Rethinking Divisive Hierarchical Clustering from a Distributional Perspective. https://arxiv.org/abs/2601.19718

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