Abstract: Outlier detection is one of the important research directions in the field of data mining. Its purpose is to find out a small portion of data in the data set that is significantly different from other data objects. Outlier detection has very important applications in the fields of network intrusion detection, credit card fraud detection, medical diagnosis and so on. Recently, rough set theory has been widely used in outlier detection. However, the classical rough set model can not effectively deal with the numerical and mixed data. Therefore, in this paper we employ the neighborhood rough set model to detect outliers, and introduce a new information entropy model——neighborhood granular entropy in neighborhood rough sets. Based on the neighborhood granularity entropy, a new outlier detection algorithm called OD_NGE is proposed. Experimental results show that OD_NGE has better outlier detection performance than the existing algorithms.

Key words: outlier detection, neighborhood rough set, knowledge granularity, neighborhood granular entropy, numeric data