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IterativeSOMSO: An Iterative Self-organizing Map for Spatial Outlier Detection

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

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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.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07307, USA

    Qiao Cai & Hong Man

  2. Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Haibo He & Jianlong Qiu

  3. School of Science, Linyi Normal University, Linyi, 276005, China

    Jianlong Qiu

Authors
  1. Qiao Cai
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  2. Haibo He
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  3. Hong Man
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  4. Jianlong Qiu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Cai, Q., He, H., Man, H., Qiu, J. (2010). IterativeSOMSO: An Iterative Self-organizing Map for Spatial Outlier Detection. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_42

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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