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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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
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