Skip to main content

Fast subset scan for spatial pattern detection

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

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.

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1467-9868.2011.01014.x

Affiliations: Carnegie Mellon University, Pittsburgh, USA

Publication date: 2012-03-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more