Spatial Data Mining with Uncertainty

Authors:
Binbin He;Cuihua Chen
Affiliations:
Institute of Geo-Spatial Information Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China;College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Venue:
Computational Intelligence and Security
Year:
2007

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Abstract

On the basis of analyzing the deficiencies of traditional spatial data mining, a framework for spatial data mining with uncertainty has been founded. Four key problems have been analyzed, including uncertainty simulation of spatial data with Monte Carlo method, spatial autocorrelation measurement, discretization of continuous data based on neighbourhood EM algorithm and uncertainty assessment of association rules. Meanwhile, the experiments concerned have been performed using the environmental geochemistry data gotten from Dexing, Jiangxi province in China.