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- Publisher Website: 10.1109/ICDM.2006.63
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Conference Paper: Efficient clustering of uncertain data
Title | Efficient clustering of uncertain data |
---|---|
Authors | |
Issue Date | 2007 |
Publisher | IEEE Computer Society. |
Citation | Proceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 436-445 How to Cite? |
Abstract | We 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 Identifier | http://hdl.handle.net/10722/93315 |
ISSN | 2020 SCImago Journal Rankings: 0.545 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ngai, WK | en_HK |
dc.contributor.author | Kao, B | en_HK |
dc.contributor.author | Chui, CK | en_HK |
dc.contributor.author | Cheng, R | en_HK |
dc.contributor.author | Chau, M | en_HK |
dc.contributor.author | Yip, KY | en_HK |
dc.date.accessioned | 2010-09-25T14:57:23Z | - |
dc.date.available | 2010-09-25T14:57:23Z | - |
dc.date.issued | 2007 | en_HK |
dc.identifier.citation | Proceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 436-445 | en_HK |
dc.identifier.issn | 1550-4786 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/93315 | - |
dc.description.abstract | We 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.language | eng | en_HK |
dc.publisher | IEEE Computer Society. | en_HK |
dc.relation.ispartof | Proceedings - IEEE International Conference on Data Mining, ICDM | en_HK |
dc.title | Efficient clustering of uncertain data | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Kao, B: kao@cs.hku.hk | en_HK |
dc.identifier.email | Cheng, R: ckcheng@cs.hku.hk | en_HK |
dc.identifier.email | Chau, M: mchau@hkucc.hku.hk | en_HK |
dc.identifier.authority | Kao, B=rp00123 | en_HK |
dc.identifier.authority | Cheng, R=rp00074 | en_HK |
dc.identifier.authority | Chau, M=rp01051 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICDM.2006.63 | en_HK |
dc.identifier.scopus | eid_2-s2.0-84868137124 | en_HK |
dc.identifier.hkuros | 137089 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-34748888305&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 436 | en_HK |
dc.identifier.epage | 445 | en_HK |
dc.identifier.scopusauthorid | Ngai, WK=14029152300 | en_HK |
dc.identifier.scopusauthorid | Kao, B=35221592600 | en_HK |
dc.identifier.scopusauthorid | Chui, CK=21741958100 | en_HK |
dc.identifier.scopusauthorid | Cheng, R=7201955416 | en_HK |
dc.identifier.scopusauthorid | Chau, M=7006073763 | en_HK |
dc.identifier.scopusauthorid | Yip, KY=7101909946 | en_HK |
dc.identifier.issnl | 1550-4786 | - |