Unsupervised classification for multivariate functional data

dc.contributor.advisorLaniado Rodas, Henryspa
dc.contributor.authorVelasco Mendoza, Javier Antonio
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.degreeMagíster en Matemáticas Aplicadasspa
dc.creator.emailjvelasc3@eafit.edu.cospa
dc.date.accessioned2019-02-14T19:12:47Z
dc.date.available2019-02-14T19:12:47Z
dc.date.issued2018
dc.description.abstractIn this paper, we propose a new method to classify functional data but in the multivariate case. This new technique is based on some order and centrality measures for the functional framework. Although our methodology works well in the general case, in this work each record is composed of two functional variables and the functional sample is composed of several records. We design a statistical tool for segmenting that sample in groups with similar characteristics. We test our methodology with real and simulated data and we highlight that this new method introduced here, work better than those techniques already introduced in the literature.spa
dc.identifier.ddc510 V433
dc.identifier.urihttp://hdl.handle.net/10784/13404
dc.language.isospaspa
dc.publisher.departmentEscuela de Ciencias. Departamento de Ciencias Básicasspa
dc.publisher.programMaestría en Matemáticas Aplicadasspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.rights.localAcceso cerradospa
dc.subjectClasificaciónspa
dc.subject.keywordMultivariate functional dataspa
dc.subject.keywordUnsupervised classificationspa
dc.subject.lembAnálisis de datosspa
dc.titleUnsupervised classification for multivariate functional dataspa
dc.typemasterThesiseng
dc.typeinfo:eu-repo/semantics/masterThesiseng
dc.type.hasVersionacceptedVersioneng
dc.type.localTesis de Maestríaspa

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