【摘要】 异常检测是数据挖掘的一个重要组成部分,其中基于密度的方法LOF是目前常用的主要方法。然而LOF方法进行检测时需要设定参数k和MinPts,检测结果对参数非常敏感,容易造成检测错误。该文提出了一种基于Voronoi图的异常检测算法VOD,采用Voronoi图来确定对象间的邻近关系,解决了基于密度方法存在的问题,算法的时间复杂性从O(N2)降低到O(NlogN)。更多还原

【Abstract】 Outlier detection is an integral part of data mining,and the density-based method LOF is the current state of the art in outlier detection.However,LOF is very sensitive to its parameter k and MinPts,which may result in wrong estimation.This paper proposes a new outlier detection algorithm based on Voronoi diagram called VOD.VOD measures the outlier factor automatically by Voronoi neighborhoods without parameter,which provides highly-accurate outlier detection and reduces the time complexity from O(N2) to O(NlogN).更多还原