Using Decision Trees for Interpretable Supervised Clustering
Natallia Kokash Leonid Makhnist
In this paper, we address an issue of finding explainable clusters of
class-uniform data in labelled datasets. The issue falls into the domain of
interpretable supervised clustering. Unlike traditional clustering, supervised
clustering aims at forming clusters of labelled data with high probability
densities. We are particularly interested in finding clusters of data of a
given class and describing the clusters with the set of comprehensive rules. We
propose an iterative method to extract high-density clusters with the help of
decisiontree-based classifiers as the most intuitive learning method, and
discuss the method of node selection to maximize quality of identified groups.