摘要： 空间数据集中离群数据与正常数据之间的非空间属性值相差较大。针对该情况，提出一种基于K-最邻近(KNN)图的空间离群点挖掘算法。该算法通过所有对象的K近邻关系构造KNN图，将相邻对象非空间属性值的差作为2个对象点间的边权值，利用裁边策略去掉权值较高的边，从而识别出空间离群点和离群区域。实验结果表明，该算法的时间性能优于POD算法。
关键词:
空间离群点,
K-最邻近图,
非空间属性值
Abstract: Aiming at the problem that the non-spatial attribute differences between outlier and normal data are very large, this paper propose a spatial outlier mining algorithm based on K-Nearest Neighbor(KNN) graph. It constructs a KNN graph based on K neighbor relationship in spatial domain, assigns the non-spatial attribute differences as edge weights, and cuts high-weight edges to identify spatial outliers and outlier region. Experimental result shows that time performance of this algorithm is superior to POD algorithm.
Key words:
spatial outlier,
K-Nearest Neighbor(KNN) graph,
non-spatial attribute value
中图分类号:
张忠平, 徐晓云, 王培. 基于KNN图的空间离群点挖掘算法[J]. 计算机工程, 2011, 37(4): 37-39.
ZHANG Zhong-Beng, XU Xiao-Yun, WANG Pei. Spatial Outlier Mining Algorithm Based on KNN Graph[J]. Computer Engineering, 2011, 37(4): 37-39.