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Multiple time series anomaly detection based on compression and correlation analysis: A medical surveillance case study

Qiao, Zhi and He, Jing and Cao, Jie and Huang, Guangyan and Zhang, Peng (2012) Multiple time series anomaly detection based on compression and correlation analysis: A medical surveillance case study. In: Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Kunming, China, April 11-13, 2012. Proceedings. Sheng, Quan Z and Wang, Guoren and Jensen, Christian S and Xu, Guandong, eds. Lecture Notes in Computer Science (7235). Springer, Heidelberg, Germany, pp. 294-305.

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Abstract

In this paper, we present a novel anomaly detection framework for multiple heterogeneous yet correlated time series, such as the medical surveillance series data. In our framework, we propose an anomaly detection algorithm from the viewpoint of trend and correlation analysis. Moreover, to efficiently process huge amount of observed time series, a new clustering-based compression method is proposed. Experimental results indicate that our framework is more effective and efficient than its peers.

Item Type: Book Section
ISBN: 9783642292521 (print) 9783642292538 (online)
Uncontrolled Keywords: ResPubID25935, data mining, knowledge discovery, pattern recognition, database management, computer communication networks
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 0806 Information Systems
Faculty/School/Research Centre/Department > Centre for Applied Informatics
Faculty/School/Research Centre/Department > College of Science and Engineering
Funders: http://purl.org/au-research/grants/arc/LP100200682
Depositing User: Yimin Zeng
Date Deposited: 09 Dec 2013 02:51
Last Modified: 10 Jul 2014 23:21
URI: http://vuir.vu.edu.au/id/eprint/22125
DOI: 10.1007/978-3-642-29253-8_25
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Citations in Scopus: 1 - View on Scopus

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