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Conference Paper: Mining order-preserving submatrices from data with repeated measurements

TitleMining order-preserving submatrices from data with repeated measurements
Authors
KeywordsAbsolute values
Computational challenges
Concurrent pattern
Data items
Data noise
Issue Date2008
PublisherIEEE Computer Society.
Citation
The 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 15-19 December 2008. In Data Mining ICDM 2008 Proceedings, 2008, p. 133-142 How to Cite?
AbstractOrder-preserving submatrices (OPSM's) have been shown useful in capturing concurrent patterns in data when the relative magnitudes of data items are more important than their absolute values. To cope with data noise, repeated experiments are often conducted to collect multiple measurements. We propose and study a more robust version of OPSM, where each data item is represented by a set of values obtained from replicated experiments. We call the new problem OPSM-RM (OPSM with repeated measurements). We define OPSM-RM based on a number of practical requirements. We discuss the computational challenges of OPSM-RM and propose a generic mining algorithm. We further propose a series of techniques to speed up two timedominating components of the algorithm. We clearly show the effectiveness of our methods through a series of experiments conducted on real microarray data.
Persistent Identifierhttp://hdl.handle.net/10722/61197
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChui, CKen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorYip, KYLen_HK
dc.contributor.authorLee, SDen_HK
dc.date.accessioned2010-07-13T03:32:58Z-
dc.date.available2010-07-13T03:32:58Z-
dc.date.issued2008en_HK
dc.identifier.citationThe 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 15-19 December 2008. In Data Mining ICDM 2008 Proceedings, 2008, p. 133-142en_HK
dc.identifier.isbn9780769535029-
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61197-
dc.description.abstractOrder-preserving submatrices (OPSM's) have been shown useful in capturing concurrent patterns in data when the relative magnitudes of data items are more important than their absolute values. To cope with data noise, repeated experiments are often conducted to collect multiple measurements. We propose and study a more robust version of OPSM, where each data item is represented by a set of values obtained from replicated experiments. We call the new problem OPSM-RM (OPSM with repeated measurements). We define OPSM-RM based on a number of practical requirements. We discuss the computational challenges of OPSM-RM and propose a generic mining algorithm. We further propose a series of techniques to speed up two timedominating components of the algorithm. We clearly show the effectiveness of our methods through a series of experiments conducted on real microarray data.en_HK
dc.languageengen_HK
dc.publisherIEEE Computer Society.-
dc.relation.ispartofData Mining ICDM 2008 Proceedingsen_HK
dc.rightsData Mining ICDM 2008 Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAbsolute values-
dc.subjectComputational challenges-
dc.subjectConcurrent pattern-
dc.subjectData items-
dc.subjectData noise-
dc.titleMining order-preserving submatrices from data with repeated measurementsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChui, CK: ckchui@cs.hku.hken_HK
dc.identifier.emailKao, B: kao@cs.hku.hk-
dc.identifier.emailYip, KYL: yuklap.yip@yale.edu-
dc.identifier.emailLee, SD: sdlee@cs.hku.hk-
dc.identifier.authorityKao, B=rp00123en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDM.2008.12en_HK
dc.identifier.scopuseid_2-s2.0-67049100145en_HK
dc.identifier.hkuros200232en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67049100145&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage133en_HK
dc.identifier.epage142en_HK
dc.publisher.placeUnited States-
dc.description.otherThe 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 15-19 December 2008. In Data Mining ICDM 2008 Proceedings, 2008, p. 133-142-
dc.identifier.scopusauthoridLee, SD=7601400741en_HK
dc.identifier.scopusauthoridYip, KY=34574226200en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridChui, CK=21741958100en_HK

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