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dc.contributor.advisorMurphy, Brendan
dc.contributor.advisorO'Regan, Myra
dc.contributor.authorMcNicholas, Paul David
dc.date.accessioned2019-05-01T13:35:45Z
dc.date.available2019-05-01T13:35:45Z
dc.date.issued2007
dc.identifier.citationPaul David McNicholas, 'Topics in unsupervised learning', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2007, pp 174
dc.identifier.otherTHESIS 8215
dc.description.abstractTwo topics in unsupervised learning are reviewed and developed; namely, model-based clustering and association rule mining. A new family of Gaussian mixture models, with a parsim onious covariance structure, is introduced. The mixtures of factor analysers and mixtures of principal component analysers models are special cases of this new family of models. This family exhibit the feature that their number of covariance parameters grows linearly with the dimensionality of the data, which leads to relatively fast computation time. These models perform excellently, compared to popular model-based clustering techniques, when applied to real data. A new family of Gaussian mixture models with a Cholesky-decomposed covariance structure is also introduced. Four members of this family are developed and applied to real data. This family of models has great potential for further development in future work. A novel approach, via association rules, is taken to the analysis of college applications data. This analysis contributes to the discussion about the existence of a 'points race'. A new method of quantifying and visualising the interestingness of an association rule is also introduced and an argument for the inclusion of negations in the association rule mining process is given.
dc.format1 volume
dc.language.isoen
dc.publisherTrinity College (Dublin, Ireland). School of Computer Science & Statistics
dc.relation.isversionofhttp://stella.catalogue.tcd.ie/iii/encore/record/C__Rb12962372
dc.subjectStatistics, Ph.D.
dc.subjectPh.D. Trinity College Dublin
dc.titleTopics in unsupervised learning
dc.typethesis
dc.type.supercollectionthesis_dissertations
dc.type.supercollectionrefereed_publications
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (Ph.D.)
dc.rights.ecaccessrightsopenAccess
dc.format.extentpaginationpp 174
dc.description.noteTARA (Trinity's Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ie
dc.identifier.urihttp://hdl.handle.net/2262/86503


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