Interpretable clustering via optimal multi-way decision trees

Abstract:Clustering is a fundamental unsupervised learning technique for uncovering data structures to facilitate knowledge discovery and decision-making. While clustering accuracy is crucial, interpretability significantly impacts the practical value of clustering results, particularly in high-risk decision-making contexts. Although decision-tree-based clustering methods offer high interpretability through explicit splitting rules, existing approaches often rely on local greedy search or require expensive computational costs limited to binary splits, resulting in deeper, less interpretable trees. To overcome these limitations, we establish a high-performance computational framework named Interpretable Clustering via Optimal Multi-way Trees (ICOMT). We make three primary contributions. First, we propose a new discretization method for numerical features using one-dimensional K-means clustering to capture data distributions. Second, we formulate a binary linear optimization (BLO) problem to guarantee tree optimality. Third, extensive validation on four public datasets demonstrates that our ICOMT method outperforms existing baselines, achieving superior clustering accuracy while maintaining shallow, concise tree structures.

Machine Learning (cs.LG)

arXiv:2602.13586 [cs.LG]

(or arXiv:2602.13586v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.13586 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.