Simultaneous clustering with mixtures of factor analysers

 

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dc.contributor.advisor Lesosky, Maia en_ZA
dc.contributor.author O'Donnell, Warwick en_ZA
dc.date.accessioned 2015-09-15T10:24:43Z
dc.date.available 2015-09-15T10:24:43Z
dc.date.issued 2013 en_ZA
dc.identifier.citation O'Donnell, W. 2013. Simultaneous clustering with mixtures of factor analysers. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/13972
dc.description.abstract 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. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Medicine en_ZA
dc.title Simultaneous clustering with mixtures of factor analysers en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Health Sciences en_ZA
dc.publisher.department Department of Medicine en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc (Med) en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation O'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/11427/13972 en_ZA
dc.identifier.chicagocitation O'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/11427/13972 en_ZA
dc.identifier.vancouvercitation O'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/11427/13972 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


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