基于反k近邻的流数据离群点挖掘算法  

Stream Data Outlier Mining Algorithm Based on Reverse k Nearest Neighbors

作　　者：张忠平[1] 梁永欣[1] 

机构地区：[1]燕山大学信息科学与工程学院,秦皇岛066004

出　　处：《计算机工程》2009年第12期11-13,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60773100);教育部科学技术研究基金资助重点项目(205014);河北省教育厅科研计划基金资助项目(2006143)

摘　　要：基于局部离群因子的增量挖掘算法需要多次扫描数据集。反k近邻适用于度量离群程度,根据该性质提出基于反k近邻的流数据离群点挖掘算法(SOMRNN)。采用滑动窗口模型更新当前窗口,仅须进行一次扫描,提高了算法效率。通过查询过程实现在任意指定时刻对当前窗口进行整体查询,及时捕捉数据流概念漂移现象。实验结果证明,SOMRNN具有适用性和有效性。Incremental mining algorithms based on local outlier factor demand multiple scans of the data set. Stream data Outlier Mining algorithm based on Reverse k Nearest Neighbors（SOMRNN） is proposed according to the concept that reverse k nearest neighbors is suitable to measure outlier degree. The sliding window is adopted to update the current window with one scan, which improves the algorithm efficiency. The capability of queries at arbitrary time on the whole current window is achieved by query manager procedure, which can capture the phenomenon of concept drift of data stream in time. Experimental results show that SOMRNN has feasibility and efficiency.

关 键 词：数据流 离群点 反k近邻 滑动窗口 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]