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

Volume

1

fYear

2006

fDate

Nov. 2006

Firstpage

797

Lastpage

800

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICCIAS.2006.294245

Filename

4072198