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Fast subset scan for spatial pattern detection
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DOI：10.1111/j.1467-9868.2011.01014.x.png)
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Abstract
En 中文
. We propose a new fast subset scan approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used functions (e.g. Kulldorff's spatial scan statistic and extensions) satisfy the linear time subset scanning property, enabling exact and efficient optimization over subsets. In the spatial setting, we demonstrate that proximity-constrained subset scans substantially improve the timeliness and accuracy of event detection, detecting emerging outbreaks of disease 2 days faster than existing methods.
Keywords:
Algorithms
Disease surveillance
Event detection
Scan statistics
Spatial scan
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
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