Kernel KMeans clustering splits for end-to-end unsupervised decision trees
Abstract
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.
Cite
@article{arxiv.2402.12232,
title = {Kernel KMeans clustering splits for end-to-end unsupervised decision trees},
author = {Louis Ohl and Pierre-Alexandre Mattei and Mickaël Leclercq and Arnaud Droit and Frédéric Precioso},
journal= {arXiv preprint arXiv:2402.12232},
year = {2024}
}