Simultaneous clustering with mixtures of factor analysers

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dc.contributor.advisorLesosky, Maiaen_ZA
dc.contributor.authorO'Donnell, Warwicken_ZA
dc.date.accessioned2015-09-15T10:24:43Z
dc.date.available2015-09-15T10:24:43Z
dc.date.issued2013en_ZA
dc.description.abstractThis work details the method of Simultaneous Model-based Clustering. It also presents an extension to this method by reformulating it as a model with a mixture of factor analysers. This allows for the technique, known as Simultaneous Model-Based Clustering with a Mixture of Factor Analysers, to be able to cluster high dimensional gene-expression data. A new table of allowable and non-allowable models is formulated, along with a parameter estimation scheme for one such allowable model. Several numerical procedures are tested and various datasets, both real and generated, are clustered. The results of clustering the Iris data find a 3 component VEV model to have the lowest misclassification rate with comparable BIC values to the best scoring model. The clustering of Genetic data was less successful, where the 2-component model could successfully uncover the healthy tissue, but partitioned the cancerous tissue in half.en_ZA
dc.identifier.apacitationO'Donnell, W. (2013). <i>Simultaneous clustering with mixtures of factor analysers</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Department of Medicine. Retrieved from http://hdl.handle.net/11427/13972en_ZA
dc.identifier.chicagocitationO'Donnell, Warwick. <i>"Simultaneous clustering with mixtures of factor analysers."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Department of Medicine, 2013. http://hdl.handle.net/11427/13972en_ZA
dc.identifier.citationO'Donnell, W. 2013. Simultaneous clustering with mixtures of factor analysers. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - O'Donnell, Warwick AB - This work details the method of Simultaneous Model-based Clustering. It also presents an extension to this method by reformulating it as a model with a mixture of factor analysers. This allows for the technique, known as Simultaneous Model-Based Clustering with a Mixture of Factor Analysers, to be able to cluster high dimensional gene-expression data. A new table of allowable and non-allowable models is formulated, along with a parameter estimation scheme for one such allowable model. Several numerical procedures are tested and various datasets, both real and generated, are clustered. The results of clustering the Iris data find a 3 component VEV model to have the lowest misclassification rate with comparable BIC values to the best scoring model. The clustering of Genetic data was less successful, where the 2-component model could successfully uncover the healthy tissue, but partitioned the cancerous tissue in half. DA - 2013 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - Simultaneous clustering with mixtures of factor analysers TI - Simultaneous clustering with mixtures of factor analysers UR - http://hdl.handle.net/11427/13972 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/13972
dc.identifier.vancouvercitationO'Donnell W. Simultaneous clustering with mixtures of factor analysers. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Department of Medicine, 2013 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13972en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMedicineen_ZA
dc.titleSimultaneous clustering with mixtures of factor analysersen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Med)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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