A framework for the uncertain spatial data mining

Binbin He; Tao Fang; Dazhi Guo

doi:10.1117/12.658220

4 January 2006 A framework for the uncertain spatial data mining

Binbin He, Tao Fang, Dazhi Guo

Proceedings Volume 5985, International Conference on Space Information Technology; 59853Z (2006) https://doi.org/10.1117/12.658220
Event: International Conference on Space information Technology, 2005, Wuhan, China

Abstract

On the basis of analyzing the uncertainties of spatial data mining (SDM), and in view of the limits of traditional spatial data mining, the framework for the uncertain spatial data mining has been founded. For which, four key problems have been probed and analyzed, including uncertainty simulation of spatial data with Monte Carlo method, measurement of spatial autocorrelation based on uncertain spatial positional data, discretization of continuous data based on neighberhood EM algorithm and quality assessment of results. Meanwhile, the experiments concerned have been performed using the geo-spatial datum gotten from 37 typified cites in China.

Citation Download Citation

Binbin He, Tao Fang, and Dazhi Guo "A framework for the uncertain spatial data mining", Proc. SPIE 5985, International Conference on Space Information Technology, 59853Z (4 January 2006); https://doi.org/10.1117/12.658220

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