基于邻域离散度的异常点检测算法-计算机科学与探索.PDF

ISSN 1673-9418 CODEN JKYTA8 E-mail: fcst@vip. 163.com Journal of Frontiers of Computer Science and Technology 1673-9418/2016/ 10(12)- 1763-10 Tel: +86-10 doi: 10.3778/j.issn. 1673-9418.1509075 基于邻域离散度的异常点检测算法* + 沈琰辉，刘华文 ，徐晓丹，赵建民，陈中育 浙江师范大学 数理与信息工程学院，浙江 金华 321004 Outlier Detection Algorithm Based on Dispersion of Neighbors + SHEN Yanhui, LIU Huawen , XU Xiaodan, ZHAO Jianmin, CHEN Zhongyu College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, China + Corresponding author: E-mail: hwliu@ SHEN Yanhui, LIU Huawen, XU Xiaodan, et al. Outlier detection algorithm based on dispersion of neigh- bors. Journal of Frontiers of Computer Science and Technology, 2016, 10(12) ：1763-1772. Abstract: Outlier detection is an important task of machine learning and data mining. A major limitation of the existing outlier detection methods is that the outlierness of border points may be very high, leading to yield misleading results in some situations. To cope with this problem, this paper proposes a novel outlier detection algorithm based on the dispersion of neighbors. The proposed algorithm adopts the dispersion of a data points neighbors as its outlier degree, thus the outlierness of border points will not be very high while the normal data and outliers can still be well distin- guished. The experimental results show the proposed algorithm is more effective in detecting outliers, less sensitive to parameter settings and is stable in terms of performance. Key words: outlier detection; machi