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Conference Paper: Discovering partial periodic patterns in discrete data sequences

TitleDiscovering partial periodic patterns in discrete data sequences
Authors
Issue Date2004
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Artificial Intelligence (Subseries Of Lecture Notes In Computer Science), 2004, v. 3056, p. 653-658 How to Cite?
AbstractThe problem of partial periodic pattern mining in a discrete data sequence is to find subsequences that appear periodically and frequently in the data sequence. Two essential subproblems are the efficient mining of frequent patterns and the automatic discovery of periods that correspond to these patterns. Previous methods for this problem in event sequence databases assume that the periods are given in advance or require additional database scans to compute periods that define candidate patterns. In this work, we propose a new structure, the abbreviated list table (ALT), and several efficient algorithms to compute the periods and the patterns, that require only a small number of passes. A performance study is presented to demonstrate the effectiveness and efficiency of our method.
Persistent Identifierhttp://hdl.handle.net/10722/93195
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorCao, Hen_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2010-09-25T14:53:46Z-
dc.date.available2010-09-25T14:53:46Z-
dc.date.issued2004en_HK
dc.identifier.citationLecture Notes In Artificial Intelligence (Subseries Of Lecture Notes In Computer Science), 2004, v. 3056, p. 653-658en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93195-
dc.description.abstractThe problem of partial periodic pattern mining in a discrete data sequence is to find subsequences that appear periodically and frequently in the data sequence. Two essential subproblems are the efficient mining of frequent patterns and the automatic discovery of periods that correspond to these patterns. Previous methods for this problem in event sequence databases assume that the periods are given in advance or require additional database scans to compute periods that define candidate patterns. In this work, we propose a new structure, the abbreviated list table (ALT), and several efficient algorithms to compute the periods and the patterns, that require only a small number of passes. A performance study is presented to demonstrate the effectiveness and efficiency of our method.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)en_HK
dc.titleDiscovering partial periodic patterns in discrete data sequencesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-7444219582en_HK
dc.identifier.hkuros103266en_HK
dc.identifier.volume3056en_HK
dc.identifier.spage653en_HK
dc.identifier.epage658en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridCao, H=7403346030en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK

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