Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2016
Abstract
This paper introduces a new unsupervised anomaly detector called ZERO++ which employs the number of zero appearances in subspaces to detect anomalies in categorical data. It is unique in that it works in regions of subspaces that are not occupied by data; whereas existing methods work in regions occupied by data. ZERO++ examines only a small number of low dimensional subspaces to successfully identify anomalies. Unlike existing frequencybased algorithms, ZERO++ does not involve subspace pattern searching. We show that ZERO++ is better than or comparable with the state-of-the-art anomaly detection methods over a wide range of real-world categorical and numeric data sets; and it is efficient with linear time complexity and constant space complexity which make it a suitable candidate for large-scale data sets.
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Publication
Journal of Artificial Intelligence Research
Volume
57
First Page
593
Last Page
620
ISSN
1076-9757
Identifier
10.1613/jair.5228
Publisher
AI Access Foundation
Citation
PANG, Guansong; TING, Kai Ming; ALBRECHT, David; and JIN, Huidong.
ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets. (2016). Journal of Artificial Intelligence Research. 57, 593-620.
Available at: https://ink.library.smu.edu.sg/sis_research/7026
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.