Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Spatial Co-location Patterns with Dynamic Neighborhood Constraint
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Efficient mining of correlation patterns in spatial point data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Spatial collocation rules are often useful for describing dependencies between spatial features. Still, the commonly used criteria for the interestingness of the rules and the selected neighbourhood constraints for spatial objects may be too rough for capturing the essentials of such dependencies. We demonstrate the difficulties with concrete examples on a large place-name data set. We propose a technique based on simple density estimation for assessing the interestingness with different neighbouring constraints.