Detecting criminal relationships through SOM visual analytics

Wang, WB; Huang, ML; Zhang, J; Lai, W

Detecting criminal relationships through SOM visual analytics

Wang, WB Huang, ML

Zhang, J

Lai, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Information Visualisation, 2015, 2015-September pp. 316 - 321
Issue Date:: 2015-09-18

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Field	Value	Language
dc.contributor.author	Wang, WB	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.contributor.author	Lai, W	en_US
dc.date.issued	2015-09-18	en_US
dc.identifier.citation	Proceedings of the International Conference on Information Visualisation, 2015, 2015-September pp. 316 - 321	en_US
dc.identifier.isbn	9781467375689	en_US
dc.identifier.issn	1093-9547	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37556
dc.description.abstract	© 2015 IEEE. Feature analysis is always beneficial to the detection of anonymous criminals in digital forensics, including people and activities, where vast amount of features extracted from databases are involved. Not all features extracted are continuous or different, some of them are discrete or have the same value with others. We discovered that using visual analytics to select features for forensic investigations is not only improve the analysis time of selection, but can also deeply and obviously display the slight changes of features and criminals and also the relationship between features and criminals in order to find the target with significant difference with others, and also predict the more active features to be used in the future. Experiments show that visual feature analysis can help to catch the desire results quickly and clearly.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Information Visualisation	en_US
dc.relation.isbasedon	10.1109/iV.2015.62	en_US
dc.title	Detecting criminal relationships through SOM visual analytics	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-September	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Barcelona
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015-September	en_US

Abstract:

© 2015 IEEE. Feature analysis is always beneficial to the detection of anonymous criminals in digital forensics, including people and activities, where vast amount of features extracted from databases are involved. Not all features extracted are continuous or different, some of them are discrete or have the same value with others. We discovered that using visual analytics to select features for forensic investigations is not only improve the analysis time of selection, but can also deeply and obviously display the slight changes of features and criminals and also the relationship between features and criminals in order to find the target with significant difference with others, and also predict the more active features to be used in the future. Experiments show that visual feature analysis can help to catch the desire results quickly and clearly.

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http://hdl.handle.net/10453/37556