In recent years

outlier detection has gained considerable interest. The identification of outliers is important for many applications such as intrusion detection

credit card fraud

criminal activities in electronic commerce

medical diagnosis and anti-terrorism

etc. The aim of outlier detection is to find small groups of objects who behave in an unexpected way or have abnormal properties when compared with the rest large amount of data. Since the existing methods for outlier detection cannot deal with uncertain and incomplete data. In this paper

we propose a new method for outlier definition and detection

which exploits the basic notion — boundary of rough sets and Knorr’s method about distance-based outliers. We also give an algorithm BDOD to find such outliers within the framework of rough set theory. The effectiveness of our algorithm is demonstrated on publicly clinical diagnosis data sets. Our method gives a new approach to the solution of uncertain and incomplete data in outlier detection.