Classification techniques for multivariate data analysis
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Authors
Lee, Jin Ki
Subjects
Multivariate analysis
Principal components analysis
Variance-covariance matrix
Correlation
Lagranian technique discriminant analysis
Distance measure (metric)
Hierarchical clustering
Nonhierarchical clustering
Similarity matrix
Principal components analysis
Variance-covariance matrix
Correlation
Lagranian technique discriminant analysis
Distance measure (metric)
Hierarchical clustering
Nonhierarchical clustering
Similarity matrix
Advisors
Richards, F. R.
Date of Issue
1980-03
Date
March 1980
Publisher
Monterey, California;. Naval Postgraduate School
Language
en_US
Abstract
The multivariate analysis techniques of cluster analysis, principal component analysis, and discriminant analysis are examined in this thesis, The theory and applications of each of the techniques are discussed. Computer software available at the Naval Postgraduate School is discussed and sample jobs are included. A hierarchical cluster analysis algorithm, available in the IMSL software package, is applied to a set of data extracted from a group of subsets for the purpose of partitioning a collection of 26 attributes of a weapon system into six clusters of super-attributes. A nonhierarchical clustering procedure, principal components analysis and discriminants were all applied to a collection of data on tanks considering of twenty-four observations of ten attributes on tanks. The cluster analysis shows that the tanks cluster somewhat naturally by nationality. The principal components analysis and the discriminant analysis show that tank weight is the single most important discriminator among nationality.
Type
Thesis
Description
Series/Report No
Department
Department of Operations Research
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.