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A LRT Framework for Fast Spacial Anomaly Detection
This video was recorded at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris 2009. Given a spatial data set placed on an n x n grid, our goal is to find the rectangular regions within which subsets of the data set 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
