OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Agency interoperation for effective data mining in border control and homeland security applications
dg.o '05 Proceedings of the 2005 national conference on Digital government research
FS3: A Random Walk Based Free-Form Spatial Scan Statistic for Anomalous Window Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Anomalous window discovery through scan statistics for linear intersecting paths (SSLIP)
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Often, it is required to identify anomalous windows along a linear path that reflect unusual rate of occurrence of a specific event of interest. Such examples include: determination of places with high number of occurrences of road accidents along a highway, leaks in natural gas transmission pipelines, pedestrian fatalities on roads, etc. In this paper, we propose a Linear Semantic Scan Statistic (LS3) approach to identify such anomalous windows along a linear path. We assume that a linear path is comprised of one-dimensional spatial locations called markers, where each marker is associated with a set of structural and behavioral attributes. We divide the linear path into linear semantic segments such that each semantic segment contains markers associated with similar structural attributes. Our goal is to identify the windows within a semantic segment whose behavioral attributes are anomalous in some sense. We accomplish this by applying the scan statistic to the behavioral attributes of the markers. We have implemented our approach by considering the real datasets of certain highways in New Jersey, USA. Our results validate that LS3 is effective in identifying high traffic accident windows.