Scalable Detection of Anomalous Patterns With Connectivity Constraints

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and exactly identify the most anomalous...

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Veröffentlicht in:	Journal of computational and graphical statistics Jg. 24; H. 4; S. 1014 - 1033
Hauptverfasser:	Speakman, Skyler, McFowland, Edward, Neill, Daniel B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 02.10.2015 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America Taylor & Francis Ltd
Schlagworte:	Biosurveillance Cluster analysis Clustering and Pattern Detection Epidemics Event detection Graph mining Graph theory Public health Scan statistics Sensors Simulation Spatial scan statistic Studies
ISSN:	1061-8600, 1537-2715
Online-Zugang:	Volltext
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