File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Reducing UK-means to k-means

TitleReducing UK-means to k-means
Authors
KeywordsAdministrative data processing
Algorithms
Boolean functions
Chlorine compounds
Clustering algorithms
Issue Date2007
PublisherIEEE, Computer Society.
Citation
The 7th IEEE International Conference on Data Mining (ICDM) Workshops 2007, Omaha, NE., 28-31 October 2007. In Proceedings of the 7th ICDM, 2007, p. 483-488 How to Cite?
AbstractThis paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to handle objects whose locations are uncertain. The location of each object is described by a probability density function (pdf). The UK-means algorithm needs to compute expected distances (EDs) between each object and the cluster representatives. The evaluation of ED from first principles is very costly operation, because the pdf's are different and arbitrary. But UK-means needs to evaluate a lot of EDs. This is a major performance burden of the algorithm. In this paper, we derive a formula for evaluating EDs efficiently. This tremendously reduces the execution time of UK-means, as demonstrated by our preliminary experiments. We also illustrate that this optimised formula effectively reduces the UK-means problem to the traditional clustering algorithm addressed by the k-means algorithm. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/129555
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorLee, SDen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorCheng, Ren_HK
dc.date.accessioned2010-12-23T08:39:15Z-
dc.date.available2010-12-23T08:39:15Z-
dc.date.issued2007en_HK
dc.identifier.citationThe 7th IEEE International Conference on Data Mining (ICDM) Workshops 2007, Omaha, NE., 28-31 October 2007. In Proceedings of the 7th ICDM, 2007, p. 483-488en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129555-
dc.description.abstractThis paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to handle objects whose locations are uncertain. The location of each object is described by a probability density function (pdf). The UK-means algorithm needs to compute expected distances (EDs) between each object and the cluster representatives. The evaluation of ED from first principles is very costly operation, because the pdf's are different and arbitrary. But UK-means needs to evaluate a lot of EDs. This is a major performance burden of the algorithm. In this paper, we derive a formula for evaluating EDs efficiently. This tremendously reduces the execution time of UK-means, as demonstrated by our preliminary experiments. We also illustrate that this optimised formula effectively reduces the UK-means problem to the traditional clustering algorithm addressed by the k-means algorithm. © 2007 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE, Computer Society.-
dc.relation.ispartofProceedings of the IEEE International Conference on Data Mining, ICDM 2007en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Data Mining. Copyright © IEEE, Computer Society.-
dc.rights©20xx 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.subjectAdministrative data processing-
dc.subjectAlgorithms-
dc.subjectBoolean functions-
dc.subjectChlorine compounds-
dc.subjectClustering algorithms-
dc.titleReducing UK-means to k-meansen_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.authorityKao, B=rp00123en_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDMW.2007.40en_HK
dc.identifier.scopuseid_2-s2.0-49549099954en_HK
dc.identifier.hkuros176467en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-49549099954&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage483en_HK
dc.identifier.epage488en_HK
dc.description.otherThe 7th IEEE International Conference on Data Mining (ICDM) Workshops 2007, Omaha, NE., 28-31 October 2007. In Proceedings of the 7th ICDM, 2007, p. 483-488-
dc.identifier.scopusauthoridLee, SD=51964193400en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats