Discover knowledge from distribution maps using Bayesian networks

Authors:
Norazwin Buang;Nianjun Liu;Terry Caelli;Rob Lesslie;Michael J. Hill
Affiliations:
Australian National University, Canberra, Australia;National ICT Australia (NICTA), Canberra Lab, ACT, Australia;National ICT Australia (NICTA), Canberra Lab, ACT, Australia;Bureau of Rural Sciences (BRS), Canberra, Australia;Bureau of Rural Sciences (BRS), Canberra, Australia
Venue:
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Year:
2006

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Abstract

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 naïve 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.