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Title: Fast Subset Scan for Spatial Pattern Detection
Summary

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.

 
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NSF-PAR ID:
10401321
Author(s) / Creator(s):
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of the Royal Statistical Society Series B: Statistical Methodology
Volume:
74
Issue:
2
ISSN:
1369-7412
Page Range / eLocation ID:
p. 337-360
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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