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Results of Discriminant Analysis with Respect to Cluster Analyses Under Dimensional Reduction
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 Title & Authors
Results of Discriminant Analysis with Respect to Cluster Analyses Under Dimensional Reduction
Chae, Seong-San;
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 Abstract
Principal component analysis is applied to reduce p-dimensions into q-dimensions ( ). Any partition of a collection of data points with p and q variables generated by the application of six hierarchical clustering methods is re-classified by discriminant analysis. From the application of discriminant analysis through each hierarchical clustering method, correct classification ratios are obtained. The results illustrate which method is more reasonable in exploratory data analysis.
 Keywords
Clustering Method;Principal Component Analysis;Discriminant Analysis;
 Language
Korean
 Cited by
1.
Application of Principal Component Analysis Prior to Cluster Analysis in the Concept of Informative Variables,;

Communications for Statistical Applications and Methods, 2003. vol.10. 3, pp.1057-1068 crossref(new window)
1.
Application of Principal Component Analysis Prior to Cluster Analysis in the Concept of Informative Variables, Communications for Statistical Applications and Methods, 2003, 10, 3, 1057  crossref(new windwow)
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