Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)

A Review of Anomaly Detection Techniques Based on Nearest Neighbor

Authors
Ming Zhao, Jingchao Chen, Yang Li
Corresponding Author
Ming Zhao
Available Online April 2018.
DOI
10.2991/cmsa-18.2018.65How to use a DOI?
Keywords
outlier detection; anomaly detection; distance based outlier detection; k nearest neighbor
Abstract

The concept of nearest neighbor has been used in several anomaly techniques, which supposes normal data instances occur in dense neighbors and anomalies occur far from their closest neighbors. So the techniques require a distance or similarity measure defined between two data instances. By now, there are several variants of basic technique extended by researchers in three different ways. The first set is to modify the definition of the anomaly score. The second set is to select different distance or density measure for different data type. The third set is to reduce the computation complexity. In this paper we have attempted to provide an overview of the previous work, although it is limited.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
Series
Advances in Intelligent Systems Research
Publication Date
April 2018
ISBN
978-94-6252-523-8
ISSN
1951-6851
DOI
10.2991/cmsa-18.2018.65How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Ming Zhao
AU  - Jingchao Chen
AU  - Yang Li
PY  - 2018/04
DA  - 2018/04
TI  - A Review of Anomaly Detection Techniques Based on Nearest Neighbor
BT  - Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
PB  - Atlantis Press
SP  - 290
EP  - 292
SN  - 1951-6851
UR  - https://doi.org/10.2991/cmsa-18.2018.65
DO  - 10.2991/cmsa-18.2018.65
ID  - Zhao2018/04
ER  -