Results of Discriminant Analysis with Respect to Cluster Analyses Under Dimensional Reduction

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

Abstract
Principal component analysis is applied to reduce p-dimensions into q-dimensions ( $\small{q {\leq} p}$). 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.
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
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
References
1.
응용통계연구, 2000. vol.13. 2, pp.329-341

2.
SAS 주성분분석, 1989.

3.
SAS 판별 및 분류분석, 1990.

4.
품질경영학회지, 1994. vol.22. 2, pp.143-153

5.
응용통계연구, 2001. vol.14. 2, pp.415-427

6.
Journal of the Korean Statistical Society, 1991. vol.20. pp.162-176

7.
Communication of Statistics Theory Method, 1987. vol.16. pp.1433-1460

8.
Cluster Analysis, 1974.

9.
Clustering Algorithms, 1975.

10.
Nature, 1966. vol.121. pp.218

11.
The Computer Journal, 1967. vol.9. pp.373-380

12.
Multivariate Analysis, 1979.

13.
Density Estimation for Statistics and Data Analysis, 1986.