Prototype based outlier detection

Outliers refer to "minority" data that are different from most other data. They usually disturb data mining process. But, sometimes they provide valuable information. Thus, it is important to identify outliers in a given data set. In this paper, we propose a novel approach which scores "outlierness" based on the distance from majority data. First, prototype data are identified. Second, those prototypes that are distant from others are eliminated. Finally, the outlierness of each data point is computed as the distance from the remaining prototypes. Experiments involving various data sets show that the proposed approach performs well in terms of accuracy, robustness and versatility.