Explaining Kernel Clustering via Decision Trees

arXiv:2402.09881v1 Announce Type: new
Abstract: Despite the growing popularity of explainable and interpretable machine learning, there is still surprisingly limited work on inherently interpretable clustering methods. Recently, there has been a surge of interest in explaining the classic k-means algorithm, leading to efficient algorithms that approximate k-means clusters using axis-aligned decision trees. However, interpretable variants of k-means have limited applicability in practice, where more flexible clustering methods are often needed to obtain useful partitions of the data. In this work, …

abstract algorithm algorithms arxiv clustering cs.lg decision decision trees kernel k-means machine machine learning trees type via work