空间聚类最优化分析(英文) |
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作者单位: | School of Electronic Information,Wuhan University |
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基金项目: | Supported by the National Natural Science Foundation of China (No.60502028, No. 90204008). |
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摘 要: | Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.
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关 键 词: | 空间聚类 最优化 分析方法 测绘数据库 |
Analysis of spatial clustering optimization |
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Authors: | Jianfeng Yang Puliu Yan Delin Xia Qing Geng |
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Institution: | (1) School of Electronic Information, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China |
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Abstract: | Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient. |
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Keywords: | data mining spatial clustering SSPG clustering optimization |
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