Outlier detection for high-dimensional data streams

Zhang, Ji and Gao, Qigang and Wang, Hai (2007) Outlier detection for high-dimensional data streams. In: 5th Dalhousie Computer Science In-house Conference (DCSI'07), 5 April 2007, Halifax, Nova Scotia, Canada.

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Abstract

[Abstract]: The explosion of data streams has sparked a lot of research interests in data mining on streaming data flow in recent years. Many data streams are inherently high dimensional and outlier detection from these data streams can potentially lead to discovery of useful abnormal and irregular patterns hidden in the streams. Outlier detection in data streams can be useful in many fields such as analysis and monitoring of network traffic data, web log, sensor networks and financial transactions.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: No
Item Status: Live Archive
Additional Information: No evidence of copyright restrictions.
Depositing User: Dr Ji Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 28 Sep 2009 04:45
Last Modified: 02 Jul 2013 23:23
Uncontrolled Keywords: data mining; outlier detection; data streams
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
URI: http://eprints.usq.edu.au/id/eprint/5639

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