A rough set approach to outlier detection

Abstract

“One person's noise is another person's signal” (Knorr and Ng 1998 Knorr, E. and Ng, R. Algorithms for mining distance-based outliers in large datasets. pp.392–403. Proceedings of the 24th VLDB Conference, New York [Google Scholar]). In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers—objects who behave in an unexpected way or have abnormal properties. Detecting such outliers is important for many applications such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations. In this paper, we suggest to exploit the framework of rough sets for detecting outliers. We propose a novel definition of outliers—RMF (rough membership function)-based outliers, by virtue of the notion of rough membership function in rough set theory. An algorithm to find such outliers is also given. And the effectiveness of RMF-based method is demonstrated on two publicly available data sets.