A Comparison of Outlier Detection Techniques for High-Dimensional Data

DOI

10.2991/ijcis.11.1.50How to use a DOI?

Keywords

data mining; outlier detection; high-dimensional data; evaluation measurement

Abstract

Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection. They can be used for conventional data analysis. However, it is not a trivial work to identify rare behaviors or patterns out from complicated data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners. Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets. Finally, several challenging issues and future research directions are discussed.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Xiaodan Xu
AU  - Huawen Liu
AU  - Li Li
AU  - Minghai Yao
PY  - 2018
DA  - 2018/01/22
TI  - A Comparison of Outlier Detection Techniques for High-Dimensional Data
JO  - International Journal of Computational Intelligence Systems
SP  - 652
EP  - 662
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.50
DO  - 10.2991/ijcis.11.1.50
ID  - Xu2018
ER  -