焦利民, 刘耀林, 任周桥. 基于自组织神经网络的空间点群聚类及其应用分析[J]. 武汉大学学报 ( 信息科学版), 2008, 33(2): 168-171.
引用本文: 焦利民, 刘耀林, 任周桥. 基于自组织神经网络的空间点群聚类及其应用分析[J]. 武汉大学学报 ( 信息科学版), 2008, 33(2): 168-171.
JIAO Limin, LIU Yaolin, REN Zhouqiao. Spatial Points Clustering Based on Self-organizing Neural Networks and Its Application[J]. Geomatics and Information Science of Wuhan University, 2008, 33(2): 168-171.
Citation: JIAO Limin, LIU Yaolin, REN Zhouqiao. Spatial Points Clustering Based on Self-organizing Neural Networks and Its Application[J]. Geomatics and Information Science of Wuhan University, 2008, 33(2): 168-171.

基于自组织神经网络的空间点群聚类及其应用分析

Spatial Points Clustering Based on Self-organizing Neural Networks and Its Application

  • 摘要: 探讨了采用自组织神经网络进行离散空间点群聚类的原理、方法及应用分析,提出了一种兼顾几何距离和属性特征的广义Euclid距离,并将其作为聚类统计量。并以实例验证了采用自组织空间聚类进行空间点群的数据分类、异常数据检验、均质区域划分等是有效的。

     

    Abstract: The principle,method and application of spatial points clustering based on self-organizing neural networks are studied.A kind of composite clustering statistic,called generalized Euclidean distance is proposed,which is calculated by both geometric and semantic characters of spatial points.Self-organizing spatial clustering based on generalized Euclidean distance can generate better result reflecting the clustering characters of spatial points.A case study to probe into data classifying,gross error detecting and homogeneous areas partitioning using self-organizing spatial clustering result is employed.

     

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