A novel outlier detection approach based on formal concept analysis

Abstract

Outlier detection is a major research field for data mining. In recent years, rough set and granular computing have been successfully applied to outlier detection, and a series of excellent results have been achieved. However, as another important area of granular computing, the study of formal concept analysis applied to outlier detection has not been reported. We introduce formal concept analysis to deal with the outlier detection problem and propose a granular concept-based outlier detection model in this paper. Firstly, we define a distance measure between objects using the object belonging or not belonging to the extent of a granular concept. Secondly, a distance measure between granular concepts and an outlier degree of each granular concept are defined. And then an outlier degree of each object is constructed from the outlier degree of the granular concept by weighted average. Finally, a granular concept-based outlier detection model is given and the related algorithm is designed. Experiments are performed on fifteen public datasets, and then our algorithm is compared with classical and rough set-based outlier detection algorithms. The results of the experiments show that the proposed algorithm can effectively and efficiently detect outliers.