# Kernel KMeans clustering splits for end-to-end unsupervised decision trees

arXiv: https://arxiv.org/abs/2402.12232 · HTML: https://mlsanity.com/paper/2402.12232/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [Deep Neural Decision Trees](https://mlsanity.com/paper/1806.06988/) (2018)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [Nearly-Tight and Oblivious Algorithms for Explainable Clustering](https://mlsanity.com/paper/2106.16147/) (2021)
- [Explainable k-means. Don't be greedy, plant bigger trees!](https://mlsanity.com/paper/2111.03193/) (2021)
- [Shallow decision trees for explainable k-means clustering](https://mlsanity.com/paper/2112.14718/) (2021)
- [Kernel k-Groups via Hartigan's Method](https://mlsanity.com/paper/1710.09859/) (2017)
- [Generalised Mutual Information for Discriminative Clustering](https://mlsanity.com/paper/2210.06300/) (2022)

## Built on by (most cited first, 4 of 4)

- [Clustering-Based Validation Splits for Model Selection under Domain Shift](https://mlsanity.com/paper/2405.19461/) (2024)
- [Explainable Clustering of Mixture Models](https://mlsanity.com/paper/2411.01576/) (2024)
- [A Tutorial on Discriminative Clustering and Mutual Information](https://mlsanity.com/paper/2505.04484/) (2025)
- [SpEx: A Spectral Approach to Explainable Clustering](https://mlsanity.com/paper/2511.00885/) (2025)

## Abstract (arXiv)

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.