A framework for the uncertain spatial data mining
Binbin He, Tao Fang, Dazhi Guo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.