Generalized dimension-reduction framework for recent-biased time series analysis

Zhao, Y; Zhang, S

Generalized dimension-reduction framework for recent-biased time series analysis

Zhao, Y Zhang, S

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Publisher:: IEEE Computer Soc
Publication Type:: Journal Article
Citation:: IEEE Transactions On Knowledge And Data Engineering, 2006, 18 (2), pp. 231 - 244
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	IEEE Transactions On Knowledge And Data Engineering, 2006, 18 (2), pp. 231 - 244	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5793
dc.description.abstract	Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper,	en_US
dc.publisher	IEEE Computer Soc	en_US
dc.relation.ispartof	IEEE Transactions On Knowledge And Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2006.30	en_US
dc.rights	© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Generalized dimension-reduction framework for recent-biased time series analysis	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	18	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
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
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	2	en_US
pubs.volume	18	en_US

Abstract:

Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper,

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http://hdl.handle.net/10453/5793