Recently, machine learning (ML) or deep learning (DL) techniques has gain more attention for prostate cancer (PCa) detection. However, DL is often described as “black boxes” and difficult to explain results. In this study, hierarchical clustering (HC),an unsupervised ML technique, was applied to multi-parametric MR to differentiate PCa. DWI (IVIM and DKI) and permeability parameters were used for HC. Comparison of HC methods was conducted. We demonstrated that HC can accurately differentiate PCa and normal tissue (PZ: 97.5%, TZ: 95.7%), with an comparable to state-of-the-art D and K. Contrary to DL, HC produces results that can be interpreted (heatmaps).