NDLI: Multi-domain anomaly detection in spatial datasets

Content Provider	Springer Nature Link
Author	Janeja, Vandana P. Palanisamy, Revathi
Copyright Year	2012
Abstract	A spatial anomaly captures a phenomenon occurring in a region which is vastly deviant in behavior with respect to the other normal observations. However, in reality this anomaly may impact other phenomena in the region across multiple domains, for example, crime is often linked to other sociopolitical factors or phenomenon such as poverty and education. Similarly, accidents in the region may be linked to other environmental factors such as weather and surface condition. So, finding anomalies across multiple domains is important in various applications. In this paper, we propose an approach for finding such a tangible anomalous window across multiple domains where window refers to the set of contiguous points in space, and since the window is multi-domain, there are several overlapping windows in the same space across domains. Our approach for finding anomalous window across the domains comprises the following steps: (1) single-domain anomaly detection: discovering anomalous window in each domain; (2) association rule mining: discovering relationship between the anomalous windows across domains using association rule mining; and (3) validation: validating the result using (a) Monte Carlo simulation, (b) correlation using lift and (c) ground truth evaluation. In addition, we also provide a probabilistic framework to evaluate the relationships between the spatial nodes as a postprocessing step. Finally, we provide a visualization technique for viewing the multi-domain anomalous window and the probabilistic relationships between the nodes. We provide detailed experimental results and comparisons with other approaches using real-world health ranking [51] and transportation datasets [50] with known ground truth windows. The results show that our approach is effective in finding the anomalies in multiple domains as compared to other approaches.
Starting Page	749
Ending Page	788
Page Count	40
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	36
Issue Number	3
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2012-10-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-domain mining Spatial anomaly detection Association rule mining Co-occurrence Information Systems and Communication Service Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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