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《Geography and Geo-Information Science》 2010-01
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Spatial Association Rules Data Mining Research on Terrain Feature and Mountain Climate Change

CHEN Gang1,2,HE Zheng-wei1,YANG Bin3(1.College of Earth Sciences,Chengdu University of Technology,Chengdu 610059;2.Information Center,Exploration & Production Research Institute,SWPB SINOPEC,Chengdu 610081;3.College of Environment and Resource,Southwest University of Science and Technology,Mianyang 621010,China)  
In this paper,aimed at problems which commonly research means,such as statistical analysis,nonlinear fitting and so on,lack the ability of extraction hidden information and processing mass data in mountain terrain feature and climate change research,the terrain and climate of Sichuan is taken as research object,and a research method which combines association rules data mining,raster image processing and terrain feature factors extraction is put forward.In this method,raster image processing and terrain analysis technique are adopt to pre-treat terrain and climate raster image by coordinate transformation,clipping,classification,factors extraction,discretization and so on,and Apriori algorithm is used to analysis terrain features and climate factors extracted by above pretreatment methods for obtaining strong association rules which represent relativity law between terrain and climate change.By means of analysis on more than 600 000 group data,totally 22 items association rules which satisfy minimum support and confidence are obtained,and 6 compound association rules are generalized by comprehensive analysis as well.Experiment shows that those results have higher credibility in reflecting associated laws between terrain feature and climate change.
【Fund】: 国家自然科学基金(40972225);; 国家科技支撑“十一五”计划(2008BAK49B02);; 国家863重点项目(2007AA120306);; 四川省杰出青年学科带头人培养计划项目(06ZQ026-014);; 四川省教育厅自然科学重点项目(2006A116)
【CateGory Index】: TP311.13
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