Advancements of outlier detection: a survey
Article
Article Title | Advancements of outlier detection: a survey |
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Article Category | Article |
Authors | |
Author | Zhang, Ji |
Journal Title | ICST Transactions on Scalable Information Systems |
Journal Citation | 13 (1), pp. 1-26 |
Number of Pages | 26 |
Year | 2013 |
Place of Publication | Brussels, Belgium |
Digital Object Identifier (DOI) | https://doi.org/10.4108/trans.sis.2013.01-03.e2 |
Web Address (URL) | http://www.eudl.eu/doi/10.4108/trans.sis.2013.01-03.e2 |
Abstract | 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. |
Keywords | data mining; outlier detection; high-dimensional datasets |
ANZSRC Field of Research 2020 | 461206. Software architecture |
469999. Other information and computing sciences not elsewhere classified | |
460605. Distributed systems and algorithms | |
Public Notes | Copyright © 2013 Zhang, licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. |
Byline Affiliations | Department of Mathematics and Computing |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q1v07/advancements-of-outlier-detection-a-survey
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