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A Power-Enhanced Algorithm for Spatial Anomaly Detection in Binary Labelled Point Data Using the Spatial Scan Statistic [postprint]

Read, S., Bath, P.A., Willett, P. and Maheswaran, R. (2010) A Power-Enhanced Algorithm for Spatial Anomaly Detection in Binary Labelled Point Data Using the Spatial Scan Statistic [postprint]. In: 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Sept 8th-10th, Cardiff UK. 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Sept 8th-10th, 2010, Cardiff, UK. Lecture Notes in Artificial Intelligence, II (6277). Springer Verlag , Berlin . (In Press)

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Abstract

This paper presents a novel modification to an existing algorithm for spatial anomaly detection in binary labeled point data sets, using the Bernoulli version of the Spatial Scan Statistic. We identify a potential ambiguity in p-values produced by Monte Carlo testing, which (by the selection of the most conservative p-value) can lead to sub-optimal power. When such ambiguity occurs, the modification uses a very inexpensive secondary test to suggest a less conservative p-value. Using benchmark tests, we show that this appears to restore power to the expected level, whilst having similarly retest variance to the original. The modification also appears to produce a small but significant improvement in overall detection performance when multiple anomalies are present.

Item Type: Proceedings Paper
Copyright, Publisher and Additional Information: This paper will appear in 'Lecture Notes in Artificial Intelligence'. Uploaded in accordance with the publisher's self-archiving policy. The published paper will be available from www.springerlink.com
Keywords: spatial scan statistic, bernoulli, power, benchmark testing, multiple clusters
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > Section of Public Health (Sheffield)
The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User: Dr Simon Read
Date Deposited: 21 Jul 2010 10:37
Last Modified: 08 Feb 2013 17:00
Status: In Press
Publisher: Springer Verlag
Refereed: Yes
URI: http://eprints.whiterose.ac.uk/id/eprint/11053

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