ECML '02 Proceedings of the 13th European Conference on Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
CART algorithm for spatial data: Application to environmental and ecological data
Computational Statistics & Data Analysis
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Mining model trees from spatial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Spatial clustering of structured objects
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Learning spatial decision tree for geographical classification: a summary of results
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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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.