Efficient mining of event-oriented negative sequential rules

Zhao, Y; Zhang, H; Cao, L; Zhang, C; Bohlscheid, H

Efficient mining of event-oriented negative sequential rules

Zhao, Y Zhang, H Cao, L

Zhang, C

Bohlscheid, H

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, 2008, pp. 336 - 342
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Bohlscheid, H	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, 2008, pp. 336 - 342	en_US
dc.identifier.isbn	9780769534961	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10723
dc.description.abstract	Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential patterns with the non-occurrence of some items, which are referred to as negative sequential patterns. This paper analyzes three types of negative sequential rules and presents a new technique to find event-oriented negative sequential rules. Its effectiveness and efficiency are shown in our experiments. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008	en_US
dc.relation.isbasedon	10.1109/WIIAT.2008.60	en_US
dc.title	Efficient mining of event-oriented negative sequential rules	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	University of Technology, Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential patterns with the non-occurrence of some items, which are referred to as negative sequential patterns. This paper analyzes three types of negative sequential rules and presents a new technique to find event-oriented negative sequential rules. Its effectiveness and efficiency are shown in our experiments. © 2008 IEEE.

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http://hdl.handle.net/10453/10723