DocumentCode :
2370635
Title :
Algorithms for spatial outlier detection
Author :
Lu, Chang-Tien ; Chen, Dechang ; Kou, Yufeng
Author_Institution :
Dept. of Comput. Sci., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
fYear :
2003
fDate :
19-22 Nov. 2003
Firstpage :
597
Lastpage :
600
Abstract :
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. 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.
Keywords :
data analysis; iterative methods; statistical databases; visual databases; iteration algorithm; median algorithm; real-world census data set; spatial data analysis; spatial outlier detection algorithm; Biometrics; Computer science; Detection algorithms; Graphics; Iterative algorithms; Object detection; Pattern analysis; Performance analysis; Scattering; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2003. ICDM 2003. Third IEEE International Conference on
Print_ISBN :
0-7695-1978-4
Type :
conf
DOI :
10.1109/ICDM.2003.1250986
Filename :
1250986
Link To Document :
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