Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Mining confident co-location rules without a support threshold
Proceedings of the 2003 ACM symposium on Applied computing
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Evaluating Attraction in Spatial Point Patterns with an Application in the Field of Cultural History
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A multiple window-based co-location pattern mining approach for various types of spatial data
International Journal of Computer Applications in Technology
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We address the problem of analyzing spatial correlation between event types in large point data sets. Collocation rules are unsatisfactory, when confidence is not a sufficiently accurate interestingness measure, and Monte Carlo testing is infeasible, when the number of event types is large. We introduce an algorithm for mining correlation patterns, based on a non-parametric bootstrap test that, however, avoids the actual resampling by scanning each point and its distances to the events in the neighbourhood. As a real data set we analyze a large place name data set, the set of event types consisting of different linguistic features that appear in the place names. Experimental results show that the algorithm can be applied to large data sets with hundreds of event types.