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Proceedings Paper

A MinMax spatial clustering algorithm under the complex geography environment
Author(s): Maoyun Guo; Zhifen Zhang; Youqiang Hu; Jianfeng Qu; Yi Chai
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Paper Abstract

The spatial clustering analysis of geographic entities under the complex geography environment is significant, such as nature resources analyzing, the public facilities location, the districts adjustment and epidemic prevention. The clustering center which is got from the clustering analyzing can be choice for the public facilities such as schools, hospitals and so on. And also such center can be the source of the epidemic, which supports to prevent the epidemic. The MinMax clustering algorithm based on the euclidian distance. But under complex geography environment such including rivers, mountains and so on, the euclidian distance can not reflect the relationship between one entities and the other. So with the analysis of the influence of road, river and the mountains to distance between the entities, the paper introduces the factors that reflects the geography environment, and improves the MinMax clustering algorithm. And the paper carries out a clustering experiment whose result shows that the improved MinMax clustering algorithm is better than the basic one.

Paper Details

Date Published: 9 January 2008
PDF: 5 pages
Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 679445 (9 January 2008); doi: 10.1117/12.784036
Show Author Affiliations
Maoyun Guo, Chongqing Univ. (China)
Zhifen Zhang, Chongqing Univ. (China)
Xichang Satellite Launch Ctr. (China)
Youqiang Hu, Chongqing Univ. (China)
Jianfeng Qu, Chongqing Univ. (China)
Yi Chai, Chongqing Univ. (China)

Published in SPIE Proceedings Vol. 6794:
ICMIT 2007: Mechatronics, MEMS, and Smart Materials
Minoru Sasaki; Gisang Choi Sang; Zushu Li; Ryojun Ikeura; Hyungki Kim; Fangzheng Xue, Editor(s)

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