Clustering for Classification

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dc.contributor.authorEvans, Reuben James Emmanuelen_NZ
dc.date.accessioned2007-07-30T09:11:51Z
dc.date.available2008-01-10T14:53:39Z
dc.date.issued2007en_NZ
dc.description.abstractAdvances in technology have provided industry with an array of devices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, so cannot be applied directly. This thesis proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationEvans, R. J. E. (2007). Clustering for Classification (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2403en
dc.identifier.urihttps://hdl.handle.net/10289/2403
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectClusteringen_NZ
dc.subjectClassificationen_NZ
dc.subjectLarge Datasetsen_NZ
dc.titleClustering for Classificationen_NZ
dc.typeThesisen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ
thesis.degree.disciplineComputing and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2007-07-30T09:11:51Zen_NZ
uow.date.available2008-01-10T14:53:39Zen_NZ
uow.date.migrated2009-06-09T23:34:46Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20070730.091151en_NZ
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