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, we investigate interpretable kernel clustering, and propose
algorithms that construct decision trees to approximate the partitions induced
by kernel k-means, a nonlinear extension of k-means. We further build on
previous work on explainable k-means and demonstrate how a suitable choice of
features allows preserving interpretability without sacrificing approximation
guarantees on the interpretable model.

探索可解释的核聚类算法，提出构建决策树来近似核 k-means 引发的分区的算法，并展示了适当选择特征如何在不损失可解释模型的近似保证的情况下保持可解释性。