Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Results of Discriminant Analysis with Respect to Cluster Analyses Under Dimensional Reduction

  • Chae, Seong-San (Department of Information and Statistics, Daejeon University)
  • Published : 2002.08.01
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Abstract

Principal component analysis is applied to reduce p-dimensions into q-dimensions ( $q {\leq} p$). 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.

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Cited by

  1. Application of Principal Component Analysis Prior to Cluster Analysis in the Concept of Informative Variables vol.10, pp.3, 2003, https://doi.org/10.5351/CKSS.2003.10.3.1057