Trees are convenient models for obtaining explainable predictions on
relatively small datasets. Although there are many proposals for the end-to-end
construction of such trees in supervised learning, learning a tree end-to-end
for clustering without labels remains an open challenge. As most works focus on
interpreting with trees the result of another clustering algorithm, we present
here a novel end-to-end trained unsupervised binary tree for clustering: Kauri.
This method performs a greedy maximisation of the kernel KMeans objective
without requiring the definition of centroids. We compare this model on
multiple datasets with recent unsupervised trees and show that Kauri performs
identically when using a linear kernel. For other kernels, Kauri often
outperforms the concatenation of kernel KMeans and a CART decision tree.

使用贪婪最大化核 KMeans 目标，无需定义质心的 Kauri 方法，是一种用于集群的新型端到端训练的无监督二叉树，它在使用线性核时表现相同，对于其他核，Kauri 通常优于核 KMeans 和 CART 决策树的串联。