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Conference Paper: Efficient clustering of uncertain data

TitleEfficient clustering of uncertain data
Authors
Issue Date2007
PublisherIEEE Computer Society.
Citation
Proceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 436-445 How to Cite?
AbstractWe study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster uncertain objects of this sort is to apply the UK-means algorithm, which is based on the traditional K-means algorithm. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration computation. We study various pruning methods to avoid such expensive expected distance calculation. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/93315
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorNgai, WKen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorChui, CKen_HK
dc.contributor.authorCheng, Ren_HK
dc.contributor.authorChau, Men_HK
dc.contributor.authorYip, KYen_HK
dc.date.accessioned2010-09-25T14:57:23Z-
dc.date.available2010-09-25T14:57:23Z-
dc.date.issued2007en_HK
dc.identifier.citationProceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 436-445en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93315-
dc.description.abstractWe study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster uncertain objects of this sort is to apply the UK-means algorithm, which is based on the traditional K-means algorithm. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration computation. We study various pruning methods to avoid such expensive expected distance calculation. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE Computer Society.en_HK
dc.relation.ispartofProceedings - IEEE International Conference on Data Mining, ICDMen_HK
dc.titleEfficient clustering of uncertain dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailKao, B: kao@cs.hku.hken_HK
dc.identifier.emailCheng, R: ckcheng@cs.hku.hken_HK
dc.identifier.emailChau, M: mchau@hkucc.hku.hken_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.identifier.authorityChau, M=rp01051en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICDM.2006.63en_HK
dc.identifier.scopuseid_2-s2.0-84868137124en_HK
dc.identifier.hkuros137089en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34748888305&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage436en_HK
dc.identifier.epage445en_HK
dc.identifier.scopusauthoridNgai, WK=14029152300en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridChui, CK=21741958100en_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK
dc.identifier.scopusauthoridChau, M=7006073763en_HK
dc.identifier.scopusauthoridYip, KY=7101909946en_HK

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