Using Decision Trees for Interpretable Supervised Clustering
This work addresses the need for interpretable supervised clustering, which is a domain-specific problem for data analysis in fields requiring transparent grouping of labeled data.
The paper tackles the problem of finding explainable, class-uniform clusters in labeled datasets by proposing an iterative decision tree-based method to extract high-density clusters and describe them with comprehensive rules.
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.