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 …

Abstract k-means and k-median clustering are powerful unsupervised machine learning
techniques. However, due to complicated dependencies on all the features, it is challenging …

We study the problem of explainable clustering in the setting first formalized by Dasgupta,
Frost, Moshkovitz, and Rashtchian (ICML 2020). A $ k $-clustering is said to be explainable …

We provide a new bi-criteria Õ (log2 k) competitive algorithm for explainable k-means
clustering. Explainable k-means was recently introduced by Dasgupta, Frost, Moshkovitz …

In recent years, much of the research on clustering algorithms has primarily focused on
enhancing their accuracy and efficiency, frequently at the expense of interpretability …

Many clustering algorithms are guided by certain cost functions such as the widely-used k-
means cost. These algorithms divide data points into clusters with often complicated …

We show that the RandomCoordinateCut algorithm gives the optimal competitive ratio for
explainable $ k $-medians in $\ell_1 $. The problem of explainable $ k $-medians was …

The domain of explainable AI is of interest in all Machine Learning fields, and it is all the
more important in clustering, an unsupervised task whose result must be validated by a …

Given a set of points in d-dimensional space, an explainable clustering is one where the
clusters are specified by a tree of axis-aligned threshold cuts. Dasgupta et al.(ICML 2020) …

Despite the growing popularity of explainable and interpretable machine learning, there is
still surprisingly limited work on inherently interpretable clustering methods. Recently, there …