Results of Discriminant Analysis with Respect to Cluster Analyses Under Dimensional Reduction Chae, Seong-San;
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.