Using Decision Trees for Interpretable Supervised Clustering

Abstract

In this paper, we address an issue of finding explainable clusters of class-uniform data in labeled datasets. The issue falls into the domain of interpretable supervised clustering. Unlike traditional clustering, supervised clustering aims at forming clusters of labeled 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 decision-tree-based classifiers as the most intuitive learning method, and discuss the method of node selection to maximize quality of identified groups.