计算机工程与应用Issue(16)：166-170,5.DOI:10.3778/j.issn.1002-8331.1308-0287
Delaunay三角剖分在离群点检测中的应用
摘要
Abstract
It is difficult to choose an appropriate parameter k when using the traditional K-nearest neighbor algorithm. In order to detect outlier points of clustering automatically and effectively, an outlier detection algorithm without parameter based on Delaunay triangulation is proposed. Delaunay triangulation is an important theory in numerical analysis and graphics. There exists an edge directly connecting each data object with its neighbor points in the Delaunay triangulation diagram, and then a neighbor relationship is established effectively. To detect the outliers, Delaunay triangulation is used to obtain the neighborhood of each point. The outlier degree is calculated according to the distribution characteristics of each point and its spatial neighbors. The outlier is determined according to its outlier factor. The experimental results show this algorithm is more effective compared with the relevant algorithm.关键词
Delaunay三角剖分/离群点/空间邻居/K 近邻Key words
Delaunay triangulation/outlier/space neighbors/K-nearest neighbor分类
信息技术与安全科学引用本文复制引用
朱庆生,唐汇,冯骥..Delaunay三角剖分在离群点检测中的应用[J].计算机工程与应用,2015,(16):166-170,5.基金项目
国家自然科学基金（No.61272194，No.61073058）。 （）
