关键词: 异常点检测, 机器学习, 数据挖掘, 主成分分析

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 point??s 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 distinguished. 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, machine learning, data mining, principal component analysis