A Review of Anomaly Detection Techniques Based on Nearest Neighbor

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/).

Download article (PDF)

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  -