An innovative outlier detection method using localized thresholds

Zhang, Ji and Cao, Jie and Zhu, Xiaodong (2012) An innovative outlier detection method using localized thresholds. Data and Knowledge Engineering. pp. 65-73. ISSN 0302-9743

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Abstract

In this paper, we investigate the research problem of specifying the thresholds to effectively detect outliers and propose an innovative technique that leverages multiple localized thresholds for detecting outliers. Our technique is able to overcome a major limitation of the existing outlier detection approaches that use a single universal threshold to distinguish outliers from normal data. The experiments conducted demonstrate that our technique is able to achieve a better detection effectiveness than the traditional approaches.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Series: Lecture Notes in Computer Science v.7696 © Springer-Verlag Berlin Heidelberg 2012. This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Permission for use must always be obtained from Springer.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 17 May 2013 05:16
Last Modified: 17 Sep 2014 04:15
Uncontrolled Keywords: distributed database; experimental evaluation; innovative systems
Fields of Research : 08 Information and Computing Sciences > 0805 Distributed Computing > 080599 Distributed Computing not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-3-642-34679-8-7
URI: http://eprints.usq.edu.au/id/eprint/22595

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