Clustering Datasets with Singular Value Decomposition

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dc.contributor.advisorLangville, Amy
dc.contributor.committeeMemberCox, Ben
dc.contributor.committeeMemberJohnston-Thom, Katherine
dc.contributor.committeeMemberJones, Martin
dc.creatorDouglas, Emmeline P.
dc.date.accessioned2016-10-18T16:13:13Z
dc.date.available2016-10-18T16:13:13Z
dc.date.issued2014-08-20
dc.description.abstractSpectral graph partitioning has been widely acknowledged as a useful way to cluster matrices. Since eigen decompositions do not exist for rectangular matrices, it is necessary to find an alternative method for clustering rectangular datasets. The Singular Value Decomposition lends itself to two convenient and effective clustering techniques, one using the signs of singular vectors and the other using gaps in singular vectors. We can measure and compare the quality of our resultant clusters using an entropy measure. When unable to decide which is better, the results can be nicely aggregated.en_US
dc.description.sponsorshipCollege of Charleston. Graduate School; College of Charleston. Department of Mathematics.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/3012
dc.language.isoen_USen_US
dc.subjectMathematics; Cluster analysisen_US
dc.titleClustering Datasets with Singular Value Decompositionen_US
dc.typeThesisen_US
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