Interpretable Data Partitioning Through Tree-Based Clustering Methods.

The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.