ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets

Pang, G; Ting, KM; Albrecht, D; Jin, H

ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets

Pang, G

Ting, KM Albrecht, D Jin, H

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Publication Type:: Journal Article
Citation:: Journal of Artificial Intelligence Research, 2016, 57 pp. 593 - 620
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Pang, G https://orcid.org/0000-0002-9877-2716	en_US
dc.contributor.author	Ting, KM	en_US
dc.contributor.author	Albrecht, D	en_US
dc.contributor.author	Jin, H	en_US
dc.date.issued	2016-12-01	en_US
dc.identifier.citation	Journal of Artificial Intelligence Research, 2016, 57 pp. 593 - 620	en_US
dc.identifier.uri	http://hdl.handle.net/10453/92328
dc.description.abstract	© 2016 AI Access Foundation. 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.	en_US
dc.relation.ispartof	Journal of Artificial Intelligence Research	en_US
dc.relation.isbasedon	10.1613/jair.5228	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets	en_US
dc.type	Journal Article
utslib.citation.volume	57	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

© 2016 AI Access Foundation. 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.

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http://hdl.handle.net/10453/92328