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In recent years, granular computing has been developed as a unified data description paradigm. As a popular soft computing supervised learning model, rough sets theory-based data description approach has been intensively investigated in data mining research. Feasible information granulation and approximation approaches have been recognized as two key features of data descriptors in rough sets. In this study, we propose a Dempster–Shafer theory-based rough granular description model based on a principle of justifiable granularity. First, we apply evidence information to show the performance of information granules generated from various data density regions, and definitions of lower and upper approximation sets are discussed considering characteristics of data credibility and plausibility, respectively. Furthermore, we propose a robust rough description model to identify some extreme instances, such as …