Abstract
Data mining refers to the extraction of knowledge from large amounts of data using pattern recognition, statistical methods, and artificial intelligence. The decision tree method, a well-known data mining technique, is used to extract decision rules because using it, we can understand the grounds of classification or prediction. The decision tree method thus can be used to analyze spatial data. There is an enormous amount of spatial data in GIS (Geographic Information System), which has been studied in modeling with these data. Spatial modeling as applied to a decision tree method can be carried out more effectively. In this paper, we extracted spatial rules using the decision tree method, and then applied them to urban growth modeling based on Cellular Automata (CA). An evaluation comparing the model using the decision tree method (the proposed model) with the standard UGM showed that the proposed model is more accurate.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, J., Kang, Y., Hong, S., Park, S. (2006). Extraction of Spatial Rules Using a Decision Tree Method: A Case Study in Urban Growth Modeling. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_25
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DOI: https://doi.org/10.1007/11892960_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
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