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Conference Paper: Online algorithms for mining inter-stream associations from large sensor networks

TitleOnline algorithms for mining inter-stream associations from large sensor networks
Authors
Loo, KKTong, IKao, CMCheung, DWL
Issue Date2005
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2005), Hanoi, Vietnam, 18-20 May 2005. In Lecture Notes In Computer Science, 2005, v. 3518, p. 143-149 How to Cite?
AbstractWe study the problem of mining frequent value sets from a large sensor network. We discuss how sensor stream data could be represented that facilitates efficient online mining and propose the interval-list representation. Based on Lossy Counting, we propose ILB, an interval-list-based online mining algorithm for discovering frequent sensor value sets. Through extensive experiments, we compare the performance of ILB against an application of Lossy Counting (LC) using a weighted transformation method. Results show that ILB outperforms LC significantly for large sensor networks. © Springer-Verlag Berlin Heidelberg 2005.
Persistent Identifierhttp://hdl.handle.net/10722/93159
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorLoo, KKen_HK
dc.contributor.authorTong, Ien_HK
dc.contributor.authorKao, CMen_HK
dc.contributor.authorCheung, DWLen_HK
dc.date.accessioned2010-09-25T14:52:41Z-
dc.date.available2010-09-25T14:52:41Z-
dc.date.issued2005en_HK
dc.identifier.citationThe 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2005), Hanoi, Vietnam, 18-20 May 2005. In Lecture Notes In Computer Science, 2005, v. 3518, p. 143-149-
dc.identifier.urihttp://hdl.handle.net/10722/93159-
dc.description.abstractWe study the problem of mining frequent value sets from a large sensor network. We discuss how sensor stream data could be represented that facilitates efficient online mining and propose the interval-list representation. Based on Lossy Counting, we propose ILB, an interval-list-based online mining algorithm for discovering frequent sensor value sets. Through extensive experiments, we compare the performance of ILB against an application of Lossy Counting (LC) using a weighted transformation method. Results show that ILB outperforms LC significantly for large sensor networks. © Springer-Verlag Berlin Heidelberg 2005.-
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleOnline algorithms for mining inter-stream associations from large sensor networksen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailKao, CM: kao@cs.hku.hken_HK
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_HK
dc.identifier.authorityKao, CM=rp00123en_HK
dc.identifier.authorityCheung, DWL=rp00101en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-26944441628-
dc.identifier.hkuros103138en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-26944441628&selection=ref&src=s&origin=recordpage-
dc.identifier.volume3518-
dc.identifier.spage143-
dc.identifier.epage149-
dc.publisher.placeGermany-
dc.identifier.scopusauthoridLoo, KK=36793892100-
dc.identifier.scopusauthoridTong, I=36916811600-
dc.identifier.scopusauthoridKao, B=35221592600-
dc.customcontrol.immutablesml 151116 - merged-

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