sis 13(1): e2

Research Article

Advancements of Outlier Detection: A Survey

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  • @ARTICLE{10.4108/trans.sis.2013.01-03.e2,
    author={Ji Zhang},
    title={Advancements of Outlier Detection: A Survey},
    journal={EAI Endorsed Transactions on Scalable Information Systems},
    volume={1},
    number={1},
    publisher={ICST},
    journal_a={SIS},
    year={2013},
    month={2},
    keywords={Data Mining, Outlier Detection, High-dimensional Datasets},
    doi={10.4108/trans.sis.2013.01-03.e2}
}
  • Ji Zhang
    Year: 2013
    Advancements of Outlier Detection: A Survey
    SIS
    ICST
    DOI: 10.4108/trans.sis.2013.01-03.e2
Ji Zhang1
  • 1: Department of Mathematics and Computing University of Southern Queensland, Australia

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
Received
2011-12-29
Accepted
2012-02-18
Published
2013-02-04
Publisher
ICST
http://dx.doi.org/10.4108/trans.sis.2013.01-03.e2

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.