欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
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。
英文摘要：
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.
同期刊论文项目
同项目期刊论文
期刊信息
位置：