Shallow decision trees for explainable $k$-means clustering. (arXiv:2112.14718v2 [cs.LG] UPDATED)

A number of recent works have employed decision trees for the construction of
explainable partitions that aim to minimize the $k$-means cost function. These
works, however, largely ignore metrics related to the depths of the leaves in
the resulting tree, which is perhaps surprising considering how the
explainability of a decision tree depends on these depths. To fill this gap in
the literature, we propose an efficient algorithm that takes into account these
metrics. In experiments on 16 datasets, our …

arxiv clustering decision lg trees