基于局部密度的快速离群点检测算法

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。

Fast outlier detection algorithm based on local density

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.