计算机工程 ›› 2010, Vol. 36 ›› Issue (1): 33-34,3.doi: 10.3969/j.issn.1000-3428.2010.01.012

• 软件技术与数据库 • 上一篇    下一篇

基于Voronoi和空间自相关的离群点检测

王 妍1,2,潘瑜春2,阎波杰2,3   

  1. (1. 首都师范大学信息工程学院,北京 100037;2. 国家农业信息化工程技术研究中心,北京 100097;3. 闽江学院地理科学系,福州 350108)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-01-05 发布日期:2010-01-05

Outlier Detection Based on Voronoi and Spatial Autocorrelation

WANG Yan1,2, PAN Yu-chun2, YAN Bo-jie2,3   

  1. (1. Information Engineering Institute, Capital Normal University, Beijing 100037; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097; 3. Department of Geography, Minjiang College, Fuzhou 350108)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-01-05 Published:2010-01-05

摘要: 为了提高空间数据挖掘的效率和准确度,在分析传统的离群点检测算法优、缺点的基础上,提出一种空间离群点检测算法。用Voronoi来确定空间对象间的邻近关系,在空间邻域内利用空间自相关性来计算局部Moran指数,并将其作为离群因子进而判断离群点。实验结果表明,该算法能够高效、准确地检测出空间离群点,具有对用户依赖性少和可伸缩性强等优点。

关键词: 空间离群点, Moran指数, 空间自相关

Abstract: In order to improve the spatial data mining efficiency and accuracy, the research on spatial outlier detection algorithm based on Voronoi and spatial autocorrelation is proposed after analyzing the advantages and disadvantages of the classical outlier detection algorithms. The algorithm calculates local Moran index of non-spatial attribute as the outlier factor by Voronoi neighborhoods without parameter. Experimental results show that the proposed algorithm can outperform other existing algorithms in detection accuracy, user dependency and efficiency.

Key words: spatial outlier, Moran index, spatial autocorrelation

中图分类号: