摘要
由于现有的空间离群点检测算法没有很好地解决空间数据的自相关性和异质性约束问题,提出用计算邻域距离的方法解决空间自相关性约束问题,用计算空间局部离群系数的方法解决空间异质性约束问题。用离群系数表示对象的离群程度,并将离群系数按降序排列,取离群系数最大的前m个对象为离群点,据此提出基于空间约束的离群点挖掘算法。实验结果表明,所提算法比已有算法具有更高的检测精度、更低的用户依赖性和更高的效率。
Major drawbacks of existing spatial outlier detection algorithms are that the spatial autocorrelation and spatial heterogeneity of spatial objects aren＇t considered, normal objects tend to be falsely detected as spatial outliers or true spatial outliers tend to be ignored. We define neighborhood distance to overcome spatial autocorrelation constraint and defined spatial local outlier factor （SLOF）to overcome spatial heterogeneity constraint. SLOF captures the local behavior of datum in their spatial neighborhood. SLOF-based algorithm of spatial outlier detection can successfully find local spatial outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. The experimental results show that our algorithm outperforms other existing algorithms in detection accuracy, user dependency and efficiency.
出处
《计算机科学》
CSCD
北大核心
2007年第6期207-209,230,共4页
Computer Science
基金
国家自然科学基金(60373069)
江苏省高校自然科学基金(05KJB520017)。
