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

arXiv:2402.12232v1 Announce Type: cross
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 …

abstract arxiv challenge clustering construction cs.ai cs.lg datasets decision decision trees focus kernel kmeans labels predictions proposals small stat.ml supervised learning tree trees type unsupervised