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dc.contributor.authorCordeiro De Amorim, Renato
dc.date.accessioned2017-01-11T20:23:22Z
dc.date.available2017-01-11T20:23:22Z
dc.date.issued2016-08-25
dc.identifier.citationCordeiro De Amorim , R 2016 , ' A survey on feature weighting based K-Means algorithms ' , Journal of Classification , vol. 33 , no. 2 , pp. 210-242 . https://doi.org/10.1007/s00357-016-9208-4
dc.identifier.issn0176-4268
dc.identifier.otherPURE: 9823144
dc.identifier.otherPURE UUID: 212b1383-c467-48d9-8d99-7fdb293e55a4
dc.identifier.otherScopus: 84983391657
dc.identifier.urihttp://hdl.handle.net/2299/17481
dc.descriptionThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016
dc.description.abstractIn a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.en
dc.format.extent32
dc.language.isoeng
dc.relation.ispartofJournal of Classification
dc.rightsEmbargoed
dc.titleA survey on feature weighting based K-Means algorithmsen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.date.embargoedUntil2017-08-25
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2016-08-25
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1007/s00357-016-9208-4
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeEmbargoed


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