Discover Knowledge From Distribution Maps Using Bayesian Networks

Buang, N., Liu, N., Caelli, T., Lesslie, R. and Hill, M.J.

This paper applies a Bayesian network to model multi criteria distribution maps and to discover knowledge contained in spatial data. The procedure consists of three steps: pre processing map data, training the Bayesian Network model using distribution maps of Australia and testing the generalization and diagnosis of the model using individual states' maps. The Bayesian network that we used in this study is known as na�ve Bayesian network. Results show that this environmental Bayesian network model can generalize the classification rules from training data for good prediction and diagnosis of a distribution map.

Cite as: Buang, N., Liu, N., Caelli, T., Lesslie, R. and Hill, M.J. (2006). Discover Knowledge From Distribution Maps Using Bayesian Networks. In Proc. Fifth Australasian Data Mining Conference (AusDM2006), Sydney, Australia. CRPIT, 61. Peter, C., Kennedy, P. J., Li, J., Simoff, S. J. and Williams, G. J., Eds. ACS. 69-74.

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