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dc.contributor.authorBass, Donald
dc.date2012
dc.date.accessioned2013-06-06T17:49:20Z
dc.date.available2013-06-06T17:49:20Z
dc.date.issued2013-06-06
dc.identifier.otherW Thesis 1391
dc.identifier.urihttp://hdl.handle.net/11040/23782
dc.descriptioni, 68 leaves : illustrations (some color).en_US
dc.descriptionIncludes bibliography (leaves 67-68).
dc.description.abstractCluster analysis is a type of machine learning used in many areas of research. Cluster validation is a method of determining a level of confidence for the results of cluster analysis. The goal of this research was to write a program trueTree that would perform cluster validation. trueTree proved successful. Every time trueTree's results were compared to past research, the results matched confirming that trueTree works properly.en_US
dc.language.isoen_USen_US
dc.publisherWheaton College (Norton, Mass.)
dc.subjectUndergraduate research
dc.subjectUndergraduate thesis
dc.subject.lcshComputer software -- Validation
dc.subject.lcshCluster analysis
dc.subject.lcshMachine learning
dc.subject.lcshParallel programs (Computer programs)
dc.subject.lcshBootstrap (Statistics)
dc.subject.lcshComputer software -- Development
dc.subject.lcshOpen source software -- Development
dc.subject.lcshLinux
dc.titleCluster validation using the non-parametric bootstrap and parallel processing: applications in unsupervised machine learning of Shimodaira's method to text mining and genomicsen_US
dc.typeThesisen_US


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