摘要
提出了基于反向K近邻(RKNN)的孤立点检测算法ODRKNN。ODRKNN算法用每个数据点的反向K近邻个数来衡量该数据点的偏离程度,在综合数据集和真实数据集上的实验结果表明,该算法能有效地检测出孤立点,且算法的效率高于算法LOF和LSC的效率。
This paper proposes a new outlier detection algorithm ODRKNN based on reverse K nearest neighbors.ODRKNN counts the number of each point＇s reverse K nearest neighbors to reflect its isolation degree,experimental results on synthesized and real world dataset show that ODRKNN can efficiently detect outliers and has higher efficiency than outliers detection algorithm LOF and LSC.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第7期182-184,共3页
Computer Engineering and Applications
关键词
孤立点
K近邻
反向K近邻
outliers
K Nearest Neighbors
Reverse K Nearest Neighbors（RKNN）
