CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The hunting of the bump: on maximizing statistical discrepancy
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Spatial scan statistics: approximations and performance study
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A LRT framework for fast spatial anomaly detection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Given a spatial dataset placed on an n ×n grid, our goal is to find the rectangular regions within which subsets of the dataset exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a likelihood ratio hypothesis test (LRT) over each rectangular region in the grid, rank all of the rectangles based on the computed LRT statistics, and return the top few most interesting rectangles. To speed this process, we develop methods to prune rectangles without computing their associated LRT statistics.