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Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set

dc.contributor.authorSousa, Áurea
dc.contributor.authorBacelar-Nicolau, Helena
dc.contributor.authorNicolau, Fernando C.
dc.contributor.authorSilva, Osvaldo
dc.date.accessioned2014-02-03T10:17:09Z
dc.date.available2014-02-03T10:17:09Z
dc.date.issued2013-06
dc.descriptionCopyright © 2013 Walter de Gruyter GmbH.en
dc.description.abstractIn this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors.en
dc.identifier.citationSousa, Áurea; Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Silva, Osvaldo (2013). "Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set". Biometrical Letters, 50(1), 27-38, ISSN (Print) 1896-3811, DOI: 10.2478/bile-2013-0015.en
dc.identifier.issn1896-3811 (Print)
dc.identifier.urihttp://hdl.handle.net/10400.3/2660
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherWalter de Gruyterpor
dc.relation.publisherversionhttp://www.au.poznan.pl/biometrical.letters/index.php?p=lissuespor
dc.subjectAscendant Hierarchical Cluster Analysisen
dc.subjectSymbolic Dataen
dc.subjectInterval Dataen
dc.subjectAffinity Coefficienten
dc.subjectVL Methodologyen
dc.titleClustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Seten
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceBerlin, Germanypor
oaire.citation.endPage38por
oaire.citation.issue(1)por
oaire.citation.startPage27por
oaire.citation.titleBiometrical Lettersen
oaire.citation.volume50por
rcaap.rightsopenAccesspor
rcaap.typearticlepor

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