Mining both positive and negative impact-oriented sequential rules from transactional data

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

Mining both positive and negative impact-oriented sequential rules from transactional data

Zhao, Y

Zhang, H Cao, L

Zhang, C

Bohlscheid, H

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5476 LNAI pp. 656 - 663
Issue Date:: 2009-07-23

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Field	Value	Language
dc.contributor.author	Zhao, Y https://orcid.org/0000-0002-0209-3971	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	2009-07-23	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5476 LNAI pp. 656 - 663	en_US
dc.identifier.isbn	3642013066	en_US
dc.identifier.isbn	9783642013065	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11926
dc.description.abstract	Traditional sequential pattern mining deals with positive correlation between sequential patterns only, without considering negative relationship between them. In this paper, we present a notion of impact-oriented negative sequential rules, in which the left side is a positive sequential pattern or its negation, and the right side is a predefined outcome or its negation. Impact-oriented negative sequential rules are formally defined to show the impact of sequential patterns on the outcome,and an efficient algorithm is designed to discover both positive and negative impact-oriented sequential rules. Experimental results on both synthetic data and real-life data show the efficiency and effectiveness of the proposed technique. © Springer-Verlag Berlin Heidelberg 2009.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-01307-2_65	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Mining both positive and negative impact-oriented sequential rules from transactional data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5476 LNAI	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Bangkok, Thailand	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
pubs.volume	5476 LNAI	en_US

Abstract:

Traditional sequential pattern mining deals with positive correlation between sequential patterns only, without considering negative relationship between them. In this paper, we present a notion of impact-oriented negative sequential rules, in which the left side is a positive sequential pattern or its negation, and the right side is a predefined outcome or its negation. Impact-oriented negative sequential rules are formally defined to show the impact of sequential patterns on the outcome,and an efficient algorithm is designed to discover both positive and negative impact-oriented sequential rules. Experimental results on both synthetic data and real-life data show the efficiency and effectiveness of the proposed technique. © Springer-Verlag Berlin Heidelberg 2009.

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

http://hdl.handle.net/10453/11926