SOMSO: a self-organizing map approach for spatial outlier detection with multiple attributes

Authors:
Qiao Cai;Haibo He;Hong Man
Affiliations:
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an effective method for visualization and cluster of high dimensional data. It can preserve intrinsic topological and metric relationships in datasets. The SOMSO method can solve high dimensional problems for spatial attributes and accurately detect spatial outliers with irregular features. The experimental results for the dataset based on U.S. population census indicate that SOMSO approach can successfully be applied in complicated spatial datasets with multiple attributes.