摘要
已有的密度离群点检测算法LOF不能适应数据分布异常情况离群点检测,INFLO算法虽引入反向k近邻点集有效地解决了数据分布异常情况的离群点检测问题,但存在需要对所有数据点不加区分地分析其k近邻和反向k近邻点集导致的效率降低问题。针对该问题,提出局部密度离群点检测算法——LDBO,引入强k近邻点和弱k近邻点概念,通过分析邻近数据点的离群相关性,对数据点区别对待;并提出数据点离群性预判断策略,尽可能避免不必要的反向k近邻分析,有效提高数据分布异常情况离群点检测算法的效率。理论分析和实验结果表明,LDBO算法效率优于INFLO,算法是有效可行的。
Mining outliers is to find exceptional objects that deviate from the most rest of the data set. Outlier detection based on density has attracted lots of attention, but the density-based algorithm named Local Outlier Factor （LOF） is not suitable for the data set with abnormal distribution, and the algorithm named INFLuenced Outlierness （INFLO） solves this problem by analyzing both k nearest neighbors and reverse k nearest neighbors of each data point at cost of inferior efficiency. To solve this problem, a local density-based algorithm named Local Density Based Outlier detection （LDBO） was proposed, which can improve outlier detection efficiency and effectiveness simultaneously. LDBO introduced definitions of strong k nearest neighbors and weak k nearest neighbors to realize outlier relation analysis of those data points located nearby. Furthermore, to improve the outlier detection efficiency, prejudgement was applied to avoid unnecessary reverse k nearest neighbor analysis as far as possible. Theoretical analysis and experimental results Indicate that LDBO outperforms INFLO in efficiency, and it is effective and feasible.
出处
《计算机应用》
CSCD
北大核心
2017年第10期2932-2937,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61370077)~~
关键词
离群点检测
局部密度
强k近邻点
弱k近邻点
反向k近邻点集
outlier detection
local density
strong k nearest neighbors
weak k nearest neighbors
Reverse k NearestNeighbors （RkNN）
