Negative sequence analysis: A review

Wang, W; Cao, L

Negative sequence analysis: A review

Wang, W

Cao, L

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Computing Surveys, 2019, 52, (2)
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Wang, W https://orcid.org/0000-0002-3561-485X
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2020-05-03T21:37:24Z
dc.date.available	2020-05-03T21:37:24Z
dc.date.issued	2019-05-01
dc.identifier.citation	ACM Computing Surveys, 2019, 52, (2)
dc.identifier.issn	0360-0300
dc.identifier.issn	1557-7341
dc.identifier.uri	http://hdl.handle.net/10453/140427
dc.description.abstract	© 2019 Association for Computing Machinery. Negative sequential patterns (NSPs) produced by negative sequence analysis (NSA) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to involving both occurring and nonoccurring items, which appear in many applications. However, the research on NSA is still at an early stage, and NSP mining involves very high computational complexity and a very large search space, there is no widely accepted problem statement on NSP mining, and different settings on constraints and negative containment have been proposed in existing work. Among existing NSP mining algorithms, there are no general and systemic evaluation criteria available to assess them comprehensively. This article conducts a comprehensive technical review of existing NSA research. We explore and formalize a generic problem statement of NSA; investigate, compare, and consolidate the definitions of constraints and negative containment; and compare the working mechanisms and efficiency of existing NSP mining algorithms. The review is concluded by discussing new research opportunities in NSA.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Computing Surveys
dc.relation.isbasedon	10.1145/3311952
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Negative sequence analysis: A review
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
dc.date.updated	2020-05-03T21:37:20Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	52
utslib.citation.issue	2

Abstract:

© 2019 Association for Computing Machinery. Negative sequential patterns (NSPs) produced by negative sequence analysis (NSA) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to involving both occurring and nonoccurring items, which appear in many applications. However, the research on NSA is still at an early stage, and NSP mining involves very high computational complexity and a very large search space, there is no widely accepted problem statement on NSP mining, and different settings on constraints and negative containment have been proposed in existing work. Among existing NSP mining algorithms, there are no general and systemic evaluation criteria available to assess them comprehensively. This article conducts a comprehensive technical review of existing NSA research. We explore and formalize a generic problem statement of NSA; investigate, compare, and consolidate the definitions of constraints and negative containment; and compare the working mechanisms and efficiency of existing NSP mining algorithms. The review is concluded by discussing new research opportunities in NSA.

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http://hdl.handle.net/10453/140427