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hal.structure.identifier
dc.contributor.authorSilva, K.P.*
hal.structure.identifier
dc.contributor.authorDe A. T. De Carvalho, Francisco*
hal.structure.identifier
dc.contributor.authorCsernel, Marc*
dc.date.accessioned2014-02-20T15:50:18Z
dc.date.available2014-02-20T15:50:18Z
dc.date.issued2009-06
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/12715
dc.language.isoenen
dc.subjectAlgorithmesen
dc.subjectClassification automatiqueen
dc.subjectAnalyse des données symboliquesen
dc.subjectclustering methodsen
dc.subject.ddc519en
dc.subject.classificationjelC89en
dc.subject.classificationjelC69en
dc.subject.classificationjelC02en
dc.titleClustering of symbolic data using the assignment-prototype algorithmen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherUniv. of Pernambuco, Recife;Brésil
dc.description.abstractenThis paper shows a fuzzy relational clustering method in order to perform the clustering of symbolic data. The presented method yields a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable dissimilarity measures. This work considers two volume-based measures that may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach. The accuracy of the results were assessed by the corrected Rand index and the overall error rate of classification.en
dc.identifier.citationpages2936-2942en
dc.relation.ispartoftitleInternational Joint Conference on Neural Networks, 2009. IJCNN 2009. Proceedingsen
dc.relation.ispartofpublnameIEEEen
dc.relation.ispartofpublcityPiscataway, NJen
dc.relation.ispartofdate2009-06
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.ispartofisbn978-1-4244-3548-7en
dc.relation.conftitleInternational Joint Conference on Neural Networks, 2009. IJCNN 2009en
dc.relation.confdate2009-06
dc.relation.confcityAtlantaen
dc.relation.confcountryÉtats-Unisen
dc.relation.forthcomingnonen
dc.identifier.doihttp://dx.doi.org/10.1109/IJCNN.2009.5178764en
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