![](/images/graphics-bg.png)
Recent Progress of Anomaly Detection
Joint Authors
Liu, Huawen
Xu, Xiaodan
Yao, Minghai
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection.
Recently, a significant number of anomaly detection methods with a variety of types have been witnessed.
This paper intends to provide a comprehensive overview of the existing work on anomaly detection, especially for the data with high dimensionalities and mixed types, where identifying anomalous patterns or behaviours is a nontrivial work.
Specifically, we first present recent advances in anomaly detection, discussing the pros and cons of the detection methods.
Then we conduct extensive experiments on public datasets to evaluate several typical and popular anomaly detection methods.
The purpose of this paper is to offer a better understanding of the state-of-the-art techniques of anomaly detection for practitioners.
Finally, we conclude by providing some directions for future research.
American Psychological Association (APA)
Xu, Xiaodan& Liu, Huawen& Yao, Minghai. 2019. Recent Progress of Anomaly Detection. Complexity،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1131267
Modern Language Association (MLA)
Xu, Xiaodan…[et al.]. Recent Progress of Anomaly Detection. Complexity No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1131267
American Medical Association (AMA)
Xu, Xiaodan& Liu, Huawen& Yao, Minghai. Recent Progress of Anomaly Detection. Complexity. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1131267
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131267