Advancements of outlier detection: a survey

Zhang, Ji (2013) Advancements of outlier detection: a survey. ICST Transactions on Scalable Information Systems, 13 (1). pp. 1-26.

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Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements in this field. The survey will not only cover the traditional outlier detection methods for static and low dimensional datasets but also review the more recent developments that deal with more complex outlier detection problems for dynamic/streaming and high-dimensional datasets.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright © 2013 Zhang, licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 17 May 2013 01:42
Last Modified: 07 Feb 2018 04:14
Uncontrolled Keywords: data mining; outlier detection; high-dimensional datasets
Fields of Research : 08 Information and Computing Sciences > 0803 Computer Software > 080302 Computer System Architecture
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0805 Distributed Computing > 080501 Distributed and Grid Systems
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.4108/trans.sis.2013.01-03.e2

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