A Comparison of Outlier Detection Techniques for High-Dimensional Data
- https://doi.org/10.2991/ijcis.11.1.50How to use a DOI?
- data mining; outlier detection; high-dimensional data; evaluation measurement
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ijcis.11.1.50 ID - Xu2018 ER -