Interpretable categorical data clustering via hypothesis testing

Lianyu Hu, Mudi Jiang, Junjie Dong, Xinying Liu, Zengyou He

Abstract: Highlights•A testing-based interpretable clustering method is presented for categorical data.•Each split for constructing the decision tree is assessed via association testing.•Our method yields concise and accurate decision trees for characterizing clusters.