In this paper we offer a new perspective on the well established
agglomerative clustering algorithm, focusing on recovery of hierarchical
structure. We recommend a simple variant of the standard algorithm, in which
clusters are merged by maximum average dot product and not, for example, by
minimum distance or within-cluster variance. We demonstrate that the tree
output by this algorithm provides a bona fide estimate of generative
hierarchical structure in data, under a generic probabilistic graphical model.
The key technical innovations are to understand how hierarchical information in
this model translates into tree geometry which can be recovered from data, and
to characterise the benefits of simultaneously growing sample size and data
dimension. We demonstrate superior tree recovery performance with real data
over existing approaches such as UPGMA, Ward's method, and HDBSCAN.

本文提出了一种新的视角来考察聚类算法，侧重于恢复层次结构，并推荐了一种通过最大平均点积合并聚类的简单变体算法，该算法在数据的概率图模型下提供了真实的层次结构估计，并通过实际数据表现出比现有方法（如 UPGMA、Ward's 方法和 HDBSCAN）更出色的树恢复性能。