Abstract
Spatial autocorrelation is the correlation among data values, strictly due to the relative location proximity of the objects that the data refer to. This statistical property clearly indicates a violation of the assumption of observation independence - a pre-condition assumed by most of the data mining and statistical models. Inappropriate treatment of data with spatial dependencies could obfuscate important insights when spatial autocorrelation is ignored. In this paper, we propose a data mining method that explicitly considers autocorrelation when building the models. The method is based on the concept of predictive clustering trees (PCTs). The proposed approach combines the possibility of capturing both global and local effects and dealing with positive spatial autocorrelation. The discovered models adapt to local properties of the data, providing at the same time spatially smoothed predictions. Results show the effectiveness of the proposed solution.
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Stojanova, D., Ceci, M., Appice, A., Malerba, D., Džeroski, S. (2011). Global and Local Spatial Autocorrelation in Predictive Clustering Trees. In: Elomaa, T., Hollmén, J., Mannila, H. (eds) Discovery Science. DS 2011. Lecture Notes in Computer Science(), vol 6926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24477-3_25
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DOI: https://doi.org/10.1007/978-3-642-24477-3_25
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