Fast subset scan for spatial pattern detection

• 成果类型: Article
• 署名作者: Neill, Daniel B.
• 署名单位: Carnegie Mellon University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2011.01014.x
• 发表日期: 2012
• 页码: 337-360
• 关键词: disease surveillance; statistics; clusters; algorithm; cancer

摘要

. 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.