Share Email Print

Proceedings Paper

A framework for the uncertain spatial data mining
Author(s): Binbin He; Tao Fang; Dazhi Guo
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 4 January 2006
PDF: 5 pages
Proc. SPIE 5985, International Conference on Space Information Technology, 59853Z (4 January 2006); doi: 10.1117/12.658220
Show Author Affiliations
Binbin He, Univ. of Electronic Science and Technology of China (China)
Tao Fang, Shanghai Jiao Tong Univ. (China)
Dazhi Guo, China Univ. of Mining and Technology (China)

Published in SPIE Proceedings Vol. 5985:
International Conference on Space Information Technology
Cheng Wang; Shan Zhong; Xiulin Hu, Editor(s)

© SPIE. Terms of Use
Back to Top