| Mining Knowledge from Result Comparison Between Spatial Clustering Themes |
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| 作者姓名： | SHAZongyao BIANFuling |
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| 作者单位： | [1]不详 [2]Ph.D,SchoolofInternationalSoftware,WuhanUniversity,129LuoyuRoad,Wuhan430079,China. |
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| 摘 要： | This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.
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| 关 键 词： | 地理信息系统 空间聚类 信息表达算法 知识开采 |
| 收稿时间： | 12 December 2004 |
Mining knowledge from result comparison between spatial clustering themes |
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| SHAZongyao BIANFuling.Mining Knowledge from Result Comparison Between Spatial Clustering Themes[J].Geo-Spatial Information Science,2005,8(1):57-63. |
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| Authors: | Sha Zongyao Ph D Bian Fuling |
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| Institution: | (1) School of International Software, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China |
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| Abstract: | This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method. |
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| Keywords: | GIS knowledge mining spatial clustering themes spatial information representation algorithms |
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