Eigenschaften und Erweiterungen der Methode CLV zum Clustern von Variablen : Anwendungen in der Sensometrie

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dc.contributor.advisorKunert, Joachimde
dc.contributor.authorSahmer, Karin
dc.contributor.refereeCazes, Pierrede
dc.date.accepted2006-10-30
dc.date.accessioned2006-11-20T12:59:07Z
dc.date.available2006-11-20T12:59:07Z
dc.date.issued2006-11-20T12:59:07Z
dc.descriptionUniversität Dortmund, Fachbereich Statistik und Université Rennes II, Haute Bretagne, Laboratoire de Statistiquede
dc.description.abstractIn this work, the properties of the method of clustering of variables around latent components (CLV) are investigated. A statistical model is postulated. This model is especially appropriate for sensory profiling data. It sheds more light on the method CLV. The clustering criterion can be expressed in terms of the parameters of the model. It is shown that, under weak conditions, the hierarchical algorithm of CLV finds the correct partition while the partitioning algorithm depends on the partition used as a starting point. Furthermore, the performance of CLV on the basis of a sample is investigated by means of a simulation study. It is shown that this performance is comparable to the performance of known methods such as the procedure Varclus of the software SAS. Finally, two methods for determining the number of groups are proposed and compared.en
dc.format.extent641940 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2003/23094
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-929
dc.identifier.urnurn:nbn:de:hbz:290-2003/23094-4
dc.language.isofr
dc.subjectClustern von Variablende
dc.subjectHauptkomponentenanalysede
dc.subjectFaktorenanalysede
dc.subjectSensorische Analysede
dc.subjectClustering of variablesen
dc.subjectPrincipal component analysisen
dc.subjectFactor analysisen
dc.subjectSensory analysisen
dc.subject.ddc310
dc.titleEigenschaften und Erweiterungen der Methode CLV zum Clustern von Variablen : Anwendungen in der Sensometriede
dc.title.alternativePropriétés et extensions de la classification de variables autour de composantes latentes. Application en évaluation sensoriellefr
dc.typeTextde
dc.type.publicationtypedoctoralThesisen
dcterms.accessRightsopen access

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