Outlier detection based on granular computing and rough set theory

• DOI: 10.1007/s10489-014-0591-4
• Publication Date: 2014-10-08T08:52:59Z
• Authors: Feng Jiang; Yu-Ming Chen
• Keywords: Credit card fraud; Local outlier factor; Identification

In recent years, outlier detection has attracted considerable attention. The identification of outliers is important for many applications, including those related to intrusion detection, credit card fraud, criminal activity in electronic commerce, medical diagnosis and anti-terrorism. Various outlier detection methods have been proposed for solving problems in different domains. In this paper, a new outlier detection method is proposed from the perspectives of granular computing (GrC) and rough set theory. First, we give a definition of outliers called GR(GrC and rough sets)-based outliers. Second, to detect GR-based outliers, an outlier detection algorithm called ODGrCR is proposed. Third, the effectiveness of ODGrCR is evaluated by using a number of real data sets. The experimental results show that our algorithm is effective for outlier detection. In particular, our algorithm takes much less running time than other outlier detection methods.