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基于自组织神经网络的空间点群聚类及其应用分析
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.
作者姓名:焦利民  刘耀林  任周桥
作者单位:1武汉大学资源与环境科学学院,武汉市珞喻路129号430079;2武汉大学地理信息系统教育部重点实验室,武汉市珞喻路129号,430079
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。

关 键 词:自组织特征映射  空间聚类  数据挖掘
文章编号:1671-8860(2008)02-0168-04
收稿时间:2007-12-20
修稿时间:2007-12-20

Spatial Points Clustering Based on Self-organizing Neural Networks and Its Application
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.
Authors:JIAO Limin  LIU Yaolin  REN Zhouqiao
Affiliation:1School of Resource and Environment Science,Wuhan University,129 Luoyu Road,Wuhan 430079,China;2 Key Laboratory of Geographic Information System,Ministry of Education,Wuhan University,129 Luoyu Road,Wuhan 430079,China
Abstract:The principle, method and application of spatial points clustering based on self-or- ganizing 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 parti- tioning using self-organizing spatial clustering result is employed.
Keywords:self-organizing feature map   spatial clustering   data mining
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