1 August 2007 Research on self-organizing clustering of spatial points
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Abstract
This paper studies the principle, method and application of spatial points clustering based on self-organizing neural networks. In this paper, we put forward a kind of composite clustering statistic, called generalized Euclidean distance, which is calculated by both geometric and semantic characters of spatial points. We propose the algorithm of spatial points clustering based on self-organizing feature map and generalized Euclidean distance. The clustering method in this paper can generate better result reflecting the clustering characters of spatial points. Finally, we employ a case study to probe into data classifying, gross error detecting and homogeneous areas partitioning using self-organizing spatial clustering result.
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Limin Jiao, Limin Jiao, Yaolin Liu, Yaolin Liu, } "Research on self-organizing clustering of spatial points", Proc. SPIE 6751, Geoinformatics 2007: Cartographic Theory and Models, 67510I (1 August 2007); doi: 10.1117/12.759520; https://doi.org/10.1117/12.759520
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