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KDD
2009
ACM

A LRT framework for fast spatial anomaly detection

14 years 4 months ago
A LRT framework for fast spatial anomaly detection
Given a spatial data set placed on an n ? 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 usersupplied 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. Categories and Subject Descriptors H.2.8 [Database Management]: Database application--data mining, spatial databases and GIS General Terms Algorithms, Experimentation
Mingxi Wu, Xiuyao Song, Chris Jermaine, Sanjay Ran
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors Mingxi Wu, Xiuyao Song, Chris Jermaine, Sanjay Ranka, John Gums
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