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acessibilidade

http://hdl.handle.net/10071/16015
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acessibilidade
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dc.contributor.authorSuleman, A.-
dc.date.accessioned2018-06-07T10:53:17Z-
dc.date.available2018-06-07T10:53:17Z-
dc.date.issued2017-
dc.identifier.isbn978-1-5090-6034-4-
dc.identifier.issn1558-4739-
dc.identifier.urihttps://ciencia.iscte-iul.pt/id/ci-pub-37991-
dc.identifier.urihttp://hdl.handle.net/10071/16015-
dc.description.abstractWe use an information-theoretic criterion to assess the goodness-of-fit of the output of archetypal analysis (AA), also intended as a fuzzy clustering tool. It is an adaptation of an existing AIC-like measure to the specifics of AA. We test its effectiveness using artificial data and some data sets arising from real life problems. In most cases, the results achieved are similar to those provided by an external similarity index. The average reconstruction accuracy is about 93%.por
dc.language.isoengpor
dc.publisherIEEEpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147442/PTpor
dc.rightsopenAccesspor
dc.subjectFuzzy clusteringpor
dc.subjectArchetypal analysispor
dc.subjectValidation indexpor
dc.titleValidation of archetypal analysispor
dc.typeconferenceObjecten_US
dc.event.title2017 IEEE International Conference on Fuzzy Systemspor
dc.event.typeConferênciapor
dc.publicationstatusPublicadopor
dc.peerreviewedyespor
dc.journal2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017en_US
degois.publication.locationNaplespor
degois.publication.title2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017por
dc.date.updated2018-06-07T10:51:54Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.1109/FUZZ-IEEE.2017.8015385-
Appears in Collections:BRU-CRI - Comunicações a conferências internacionais

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