Algorithms for spatial outlier detection

Chang Tien Lu*, Dechang Chen, Yufeng Kou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

127 Scopus citations


A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. One drawback of existing methods is that normal objects tend to be falsely detected as spatial outliers when their neighborhood contains true spatial outliers. In this paper, we propose a suite of spatial outlier detection algorithms to overcome this disadvantage. We formulate the spatial outlier detection problem in a general way and design algorithms which can accurately detect spatial outliers. In addition, using a real-world census data set, we demonstrate that our approaches can not only avoid detecting false spatial outliers but also find true spatial outliers ignored by existing methods.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Number of pages4
StatePublished - 2003
Externally publishedYes
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: 19 Nov 200322 Nov 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference3rd IEEE International Conference on Data Mining, ICDM '03
Country/TerritoryUnited States
CityMelbourne, FL


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