LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours

Pang, G; Ting, KM; Albrecht, D

LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours

Pang, G

Ting, KM Albrecht, D

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, 2016, pp. 623 - 630
Issue Date:: 2016-01-29

Closed Access

	Filename	Description	Size
	Paper.pdf	Published version	259.4 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.date.issued	2016-01-29	en_US
dc.identifier.citation	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, 2016, pp. 623 - 630	en_US
dc.identifier.isbn	9781467384926	en_US
dc.identifier.uri	http://hdl.handle.net/10453/104414
dc.description.abstract	© 2015 IEEE. We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time complexity with respect to data size and the number of dimensions, and it is one of the few anomaly detectors which can apply directly to both numeric and categorical data sets. Our extensive empirical evaluation shows that LeSiNN is either competitive to or better than six state-of-the-art anomaly detectors in terms of detection accuracy and runtime.	en_US
dc.relation.ispartof	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015	en_US
dc.relation.isbasedon	10.1109/ICDMW.2015.62	en_US
dc.title	LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours	en_US
dc.type	Conference Proceeding
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

Abstract:

© 2015 IEEE. We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time complexity with respect to data size and the number of dimensions, and it is one of the few anomaly detectors which can apply directly to both numeric and categorical data sets. Our extensive empirical evaluation shows that LeSiNN is either competitive to or better than six state-of-the-art anomaly detectors in terms of detection accuracy and runtime.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/104414