Dissertation/Thesis Abstract

Clustering datasets with singular value decomposition
by Douglas, Emmeline P., M.S., College of Charleston, 2008, 85; 1461189
Abstract (Summary)

Spectral 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.

Indexing (document details)
Advisor: Langville, Amy N.
Commitee: Cox, Ben, Johnston-Thom, Katherine, Jones, Martin
School: College of Charleston
Department: Mathematics
School Location: United States -- South Carolina
Source: MAI 47/03M, Masters Abstracts International
Source Type: DISSERTATION
Subjects: Mathematics
Keywords: Cluster aggregation, Clustering, Matrix analysis, Singular value decomposition
Publication Number: 1461189
ISBN: 978-0-549-97263-1
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest