The traditional distance-based outlier detection method can not effectively deal with the data sets containing
discrete attributes. Therefore, the distance-based outlier detection method to rough sets is introduced, and the advantage of
rough sets is taken to solve the problem of dealing with discrete attributes. First, three distance metrics for discrete attributes
within the framework of rough sets are proposed. Second, for each of these distance metrics, a corresponding outlier detection
algorithm is designed, to detect outliers from data sets containing discrete attributes. Finally, the feasibility and effectiveness
of these algorithms are demonstrated on two UCI data sets containing discrete attributes.