Clustering using Unsupervised Binary Trees: CUBT

TL;DR

The paper introduces CUBT, an interpretable clustering method based on unsupervised binary trees, involving recursive splitting, pruning, and joining, with proven consistency and tested on various datasets.

Contribution

It presents a novel three-stage clustering approach using unsupervised binary trees, combining interpretability with theoretical consistency guarantees.

Findings

Consistent clustering results demonstrated on simulated data.

Effective clustering performance on real datasets.

Method provides interpretable cluster structures.

Abstract

We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.