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Detection of cross-channel anomalies from multiple data channels

Pham, Duc-Son, Saha, Budhaditya, Phung, Dinh Q. and Venkatesh, Svetha 2011, Detection of cross-channel anomalies from multiple data channels, in ICDM 2011 : Proceedings of the IEEE International Conference on Data Mining, IEEE, [Washington, D. C.], pp. 527-536, doi: 10.1109/ICDM.2011.51.

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Title Detection of cross-channel anomalies from multiple data channels
Author(s) Pham, Duc-Son
Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Data Mining (11th : 2011 : Vancouver, B. C.)
Conference location Vancouver, B. C.
Conference dates 11-14 Dec. 2011
Title of proceedings ICDM 2011 : Proceedings of the IEEE International Conference on Data Mining
Editor(s) [Unknown]
Publication date 2011
Conference series International Conference on Data Mining
Start page 527
End page 536
Total pages 10
Publisher IEEE
Place of publication [Washington, D. C.]
Keyword(s) anomaly detection
Spectral methods
topic detection
Summary We identify and formulate a novel problem: crosschannel anomaly detection from multiple data channels. Cross channel anomalies are common amongst the individual channel anomalies, and are often portent of significant events. Using spectral approaches, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single channel anomalies. Our mathematical analysis shows that our method is likely to reduce the false alarm rate. We demonstrate our method in two applications: document understanding with multiple text corpora, and detection of repeated anomalies in video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large scale data stream analysis.
ISBN 9781457720758
ISSN 1550-4786
Language eng
DOI 10.1109/ICDM.2011.51
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044906

Document type: Conference Paper
Collection: School of Information Technology
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