期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B2
页码：219-224
出版社：Copernicus Publications
摘要：This paper proposes a spatial clustering model based on self-organizing feature map and a composite distance measure, and studies the knowledge discovery from spatial database of spatial objects with non-spatial attributes. This paper gives the structure, algorithm of the spatial clustering model based on SOFM. We put forward a composite clustering statistic, which is calculated by both geographical coordinates and non-spatial attributes of spatial objects, and revising the learning algorithm of self-organizing clustering model. The clustering model is unsupervised learning and self-organizing, no need to pre-determine all clustering centers, having little man-made influence, and shows more intelligent. The composite distance based spatial clustering can lead to more objective results, indicating inherent domain knowledge and rules. Taking urban land price samples as a case, we perform domain knowledge discovery by the spatial clustering model. First, we implement several spatial clustering for multi-purpose, and find series spatial classifications of points with non-spatial attributes from multi-angle of view. Then we detect spatial outliers by the self-organizing clustering result based on composite distance statistic along with some spatial analysis technologies. We also get a meaningful and useful spatial homogeneous area partition of the study area
关键词：Self-Organizing Feature Map; Spatial Clustering; Knowledge Discovery; Data Mining
