Title :
Spatial Data Mining with Uncertainty
Author :
He, Binbin ; Chen, Cuihua
Author_Institution :
Inst. of Geo-Spatial Inf. Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu
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
Keywords :
Monte Carlo methods; data mining; expectation-maximisation algorithm; uncertainty handling; visual databases; Monte Carlo method; association rules; continuous data discretization; neighbourhood EM algorithm; spatial autocorrelation measurement; spatial data mining; uncertainty assessment; uncertainty simulation; Algorithm design and analysis; Analytical models; Association rules; Autocorrelation; Data mining; Global Positioning System; Helium; Information analysis; Information science; Uncertainty;
Conference_Titel :
Computational Intelligence and Security, 2006 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
1-4244-0605-6
Electronic_ISBN :
1-4244-0605-6
DOI :
10.1109/ICCIAS.2006.294245
