IterativeSOMSO: an iterative self-organizing map for spatial outlier detection

Authors:
Qiao Cai;Haibo He;Hong Man;Jianlong Qiu
Affiliations:
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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Abstract

In this paper, we propose an iterative self-organizing map approach for spatial outlier detection (IterativeSOMSO) IterativeSOMSO method can address high dimensional problems for spatial attributes and accurately detect spatial outliers with irregular features Detection of spatial outliers facilitates further discovery of spatial distribution and attribute information for data mining problems The experimental results indicate our proposed approach can be effectively implemented for the large spatial dataset based on U.S Census Bureau with approving performance.