一种改进的LOF异常点检测算法  

An Improved LOF Outlier Detection Algorithm

作　　者：周鹏 程艳云[1] 

机构地区：[1]南京邮电大学自动化学院,江苏南京210023

出　　处：《计算机技术与发展》2017年第12期115-118,共4页Computer Technology and Development

基　　金：江苏省自然科学基金(BK20140877;BK2014803)

摘　　要：LOF异常点检测算法在实际应用中有两个缺陷:一是离群因子值只与参数K有关,当K取值不同时,离群因子的值将不同,之前是异常点的数据可能不再是异常点。二是对于未知异常点个数的数据集,选择参数K以保证离群点的挖掘数量合理难以做到。因此,提出一种结合平均密度的改进LOF异常点检测算法。首先分析数据集中数据点的平均密度,根据密度的分布情况确定数据集的异常点个数M1及异常集D1,然后通过计算离群因子确定M2(M2=M1)个异常点及异常集D2。取D1与D2的交集作为最终的离群集。实验结果表明,改进算法在检测精准性方面有显著提高,误报率较低,综合评价指标F值比LOF算法有显著增强。In practical application,LOF,an anomaly detection algorithm,has two defects. One is the outlier factor value only related to the parameter K. When K is changed,the value will be different from before and an abnormal point may be a normal point. Another is for a data set with unknown abnormal points. It is very hard to choose a suitable parameter K to ensure reasonable mining number of outlier points. Therefore, an improved LOF combined with the average density is proposed. Firstly,the average density of each point is analyzed, and the number of abnormal points （ Ml ） and abnormal set （ D1 ） are determined according to the distribution of average density in the data set. Then M2 （ M2 = M1 ）, another number of abnormal points, and D2, another abnormal set,are ensured through calculating the value of outlier factor. The intersection of Dl and D2 is taken as the final result. Experiment shows that the improved algorithm can improve the detection precision remarkably with lower false rate, and is superior to LOF on the comprehensive evaluation index F.

关 键 词：LOF算法 平均密度 异常点集 离群因子 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]