28 October 2006 Bayesian network approach to spatial data mining: a case study
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Proceedings Volume 6421, Geoinformatics 2006: Geospatial Information Technology; 64211T (2006) https://doi.org/10.1117/12.713155
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
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.
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Jiejun Huang, Youchuan Wan, "Bayesian network approach to spatial data mining: a case study", Proc. SPIE 6421, Geoinformatics 2006: Geospatial Information Technology, 64211T (28 October 2006); doi: 10.1117/12.713155; https://doi.org/10.1117/12.713155
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