Efficient Mining of Correlation Patterns in Spatial Point Data

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.