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Application of Principal Component Analysis Prior to Cluster Analysis in the Concept of Informative Variables
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 Title & Authors
Application of Principal Component Analysis Prior to Cluster Analysis in the Concept of Informative Variables
Chae, Seong-San;
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 Abstract
Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.
 Keywords
Agglomerative Clustering Algorithm;Principal Component Analysis;Informative Variables;
 Language
English
 Cited by
 References
1.
Journal of the Korean Statistical Society, 1991. vol.20. pp.162-176

2.
The Korean Communication in Sttistics, 2002. vol.9. pp.543-554 crossref(new window)

3.
Applied Statistics, 1983. vol.32. 3, pp.267-275 crossref(new window)

4.
The Canadian Journal of Statistics, 1979. vol.7. pp.29-38 crossref(new window)

5.
Communications in Statistics, Theory and Method, 1987. vol.16. pp.1433-1460 crossref(new window)

6.
Journal of Classification, 1988. vol.5. pp.205-228 crossref(new window)

7.
Applied Multivariate Statistical Analysis, 1982.

8.
Nature, 1966. pp.212-218

9.
The Computer Journal, 1967. vol.9. pp.373-380 crossref(new window)

10.
Proceedings of National Academy Sciences in United State of America, 1999. vol.96. pp.9212-9217 crossref(new window)

11.
Journal of the American Statistical Association, 1971. vol.66. pp.846-850 crossref(new window)

12.
Molecular Biology of the Cell, 1998. vol.9. pp.3273-3297 crossref(new window)

13.
Technical Report, Stanford University, 1999.