Hierarchical data analysis using discernibility relation

Human-in-the-Loop (HITL) machine learning uses human feedback to improve performance of machine learning models. One of the focuses in HITL machine learning research is to explore ways to capture human feedback and transform feedback to useful information that can inform the learning process. This paper outlines a clustering method that is based on discernibility relation in rough set theory. This clustering method presents intermediate clustering results using indiscernibility definition graph. Human users can provide feedback by manipulating the cluster representatives that are presented in an indiscernibility definition graph. Discernibility relation offers a more intuitive understanding of clustering results when compared to distance-based relationship in terms of providing useful feedback to inform the clustering algorithm about its performance.