2009 ALRTFrameworkforFastSpatialAnom

• (Wu et al., 2009) ⇒ Mingxi Wu, Xiuyao Song, Chris Jermaine, Sanjay Ranka, and John Gums. (2009). “A LRT Framework for Fast Spatial Anomaly Detection.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557116

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• http://scholar.google.com/scholar?q=%22A+LRT+framework+for+fast+spatial+anomaly+detection%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557116&preflayout=flat#citedby

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Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 ALRTFrameworkforFastSpatialAnom	Mingxi Wu Xiuyao Song John Gums Sanjay Ranka Chris Jermaine			A LRT Framework for Fast Spatial Anomaly Detection		KDD-2009 Proceedings		10.1145/1557019.1557116		2009