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基于空间相邻关系的GML点对象离群检测算法 被引量：4

An Algorithm for GML Point Outlier Detection Based on Space Adjacent Relations
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摘要 提出了一种基于空间相邻关系的点对象离群检测算法SAOD(Space Adjacent Relations Based GML Point Outlier Detec-tion Algorithm).利用空间相邻关系作为空间点对象的相似度度量准则,得到相似度矩阵,从而挖掘GML中的离群点对象.实验结果表明,SAOD算法能有效地检测GML中的离群点对象并且具有较高的效率. At present algorithm for GML outlier detection has seldom been researched. Algorithm SAOD for GML point outlier detection based on space adjacent relations is proposed in this paper. In this algorithm, the space adjacent relations between spatial points are considered as the similarity measurement and similarity matrix is computed. The expected outliers can be obtained from the matrix. The results of experiments show that algorithm SAOD is effective and efficient.
作者 陈佳春 吉根林
机构地区 南京师范大学数学与计算机科学学院 南京师范大学虚拟地理环境教育部重点实验室
出处 《南京师范大学学报（工程技术版）》 CAS 2009年第1期61-63,共3页 Journal of Nanjing Normal University(Engineering and Technology Edition)
基金 国家自然科学基金(40771163 40871176)资助项目
关键词 离群点检测 空间相邻 GML数据挖掘 outlier detection, space adjacent relations, GML data mining
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献4

  • 1Barnett V,Lewis T.Outliers in Statistical Data[M].New York:John Wiley & Sons,1994:194-223.
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同被引文献30

  • 1薛安荣,鞠时光.基于空间约束的离群点挖掘[J].计算机科学,2007,34(6):207-209. 被引量：12
  • 2Knorr E,Ng R.Finding intensional knowledge of distance-based outliers[C] // Proc of the 25th Verg Large Databases Conference.Edinburgh:Morgan Kaufmann Publishers,1999:211-222.
  • 3Breunig M M,Kriegel H P,Ng R T,et al.Optics of:identifying density-based local outliers[C] // Proc of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases,Lecture Notes in Computer Science 1704.Prague:Springer,1999:262-270.
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  • 6Sheng Yijiang,Qing Boan.Clustering-based outlier detection method[C] // 5 th International Conference on Fuzzy Systems and Knowledge Discovery.Piscataway:IEEE Computer Society,2008:429-433.
  • 7张书亮,闾国年,龚健雅,等.地理标示语言--Geo-Web基础[M].北京:科学出版社,2008:3-4.
  • 8Ester M,Kriegel H P,Jorg S,et al.A density-based algorithm for discovering clustering in large spatial databases with noise[C] // Proceeding of 2nd Conference on Knowledge Discovery in Databases.Piscataway:IEEE Press,1996:226-231.
  • 9Barnett V,Lewis T.Outliers in Statistical Data[M].New York:John Wiley & Sons,1994.
  • 10Barnett V,Lewis T. Outliers in statistical data. New York: John Wiley and Sons, 1994.

引证文献4

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南京师范大学学报（工程技术版）
2009年 第1期

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