Spatial outlier detection based on iterative self-organizing learning model
Date of Original Version
In this paper, we propose an iterative self-organizing map (SOM) approach with robust distance estimation (ISOMRD) for spatial outlier detection. Generally speaking, spatial outliers are irregular data instances which have significantly distinct non-spatial attribute values compared to their spatial neighbors. In our proposed approach, we adopt SOM to preserve the intrinsic topological and metric relationships of the data distribution to seek reasonable spatial clusters for outlier detection. The proposed iterative learning process with robust distance estimation can address the high dimensional problems of spatial attributes and accurately detect spatial outliers with irregular features. To verify the efficiency and robustness of our proposed algorithm, comparative study of ISOMRD and several existing approaches are presented in detail. Specifically, we test the performance of our method based on four real-world spatial datasets. Various simulation results demonstrate the effectiveness of the proposed approach. © 2013 Elsevier B.V.
Cai, Qiao, Haibo He, and Hong Man. "Spatial outlier detection based on iterative self-organizing learning model." Neurocomputing 117, (2013): 161-172. doi:10.1016/j.neucom.2013.02.007.