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Proceedings Paper

Bayesian network approach to spatial data mining: a case study
Author(s): Jiejun Huang; Youchuan Wan
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Paper Abstract

Spatial data mining is a process of discovering interesting, novel, and potentially useful information or knowledge hidden in spatial data sets. It involves different techniques and different methods from various areas of research. A Bayesian network is a graphical model that encodes causal probabilistic relationships among variables of interest, which has a powerful ability for representing and reasoning and provides an effective way to spatial data mining. In this paper we give an introduction to Bayesian networks, and discuss using Bayesian networks for spatial data mining. We propose a framework of spatial data mining based on Bayesian networks. Then we show a case study and use the experimental results to validate the practical viability of the proposed approach to spatial data mining. Finally, the paper gives a summary and some remarks.

Paper Details

Date Published: 28 October 2006
PDF: 6 pages
Proc. SPIE 6421, Geoinformatics 2006: Geospatial Information Technology, 64211T (28 October 2006); doi: 10.1117/12.713155
Show Author Affiliations
Jiejun Huang, Wuhan Univ. of Technology (China)
Youchuan Wan, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 6421:
Geoinformatics 2006: Geospatial Information Technology
Huayi Wu; Qing Zhu, Editor(s)

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