基于全局最近邻的离群点检测算法

Outlier detection algorithm based on global nearest neighborhood

针对全局最近邻离群点检测算法的效率问题,为了能够在数据集中快速准确地检测离群点,运用属性约简技术,将离群点的搜索简约到较小的最具代表性的属性子空间中进行,从而有效降低属性空间搜索的复杂度。在此基础上,通过计算基于近邻的加权离群因子实现离群点的检测并提出了相应的算法。实验表明,该离群点算法具有较好的适应性和有效性。

Traditional outlier detection algorithms fall short in efficiency for their holistic nearest neighboring search mechanism and need to be improved.This paper proposed a new outlier detection method using attribute reduction techniques which enabled the algorithm to focus its detecting scope only on the most meaningful attributes of the data space.Under the reduced set of attributes,a concept of neighborhood-based outlier factor was defined for the algorithm to judge data＇s abnormity.The combined strategy can reduce the searching complexity significantly and find more reasonable outliers in dataset.The results of experiments also demonstrate promising adaptability and effectiveness of the proposed approach.