Correspondence analysis and two-way clustering

SORT

Volumn 29, Issue 1, 2005, Pages 27-41

  Ciampi, Antonio ^a   Marcos, Ana González ^b   Limas, Manuel Castejón ^b  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITY OF LEÓN   (Spain)

Author keywords

Block clustering; Data visualization; Selecting number of axes

Indexed keywords

EID: 23844553154     PISSN: 16962281     EISSN: 20138830     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Bengio, Y., Vincent, P., Paiement, J-F., Delalleau, O., Ouimet, M., and Le Roux, N. (2003). 'Spectral clustering and Kernel PCA are learning eigenfunctions'. Technical Report 1239, Département d'Informatique et Recherche Opérationnelle, Université de Montréal. http://www.iro.umontreal.ca/∼lisa/publications.html.
2. Caussinus, H. & Ruiz-Gazen, A. (1995). Metrics for finding typical structures by means of principal component analysis. In Data Science and its Applications, Y. Escoufier & C. Hayashi (eds)., Tokyo: Academic Press, 177-192.
3. Caussinus, H. & Ruiz-Gazen, A. (2003). Which structures do generalized principal component analysis display? The case of multiple correspondence analysis. To appear in Multiple Correspondence Analysis and Related Methods (eds. Michael Greenacre and Jörg Blasius), London: Chapman & Hall, 2006.
4. Gordon, A.D. (1999). Classification, 2nd Edition, London: Chapman & Hall.
5. Greenacre, M.J. (1984). Theory and Application of Correspondence Analysis, London: Academic Press.
6. Greenacre, M. (1993). Correspondence Analysis in Practice. London: Academic Press.
7. Greenacre, M. (2000). Correspondence analysis of a square symmetric matrix. Applied Statistics, 49, 297-310.
8. Ng, A. Y., Jordan, M. I., and Y. Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems (NIPS), T. Dietterich, S. Becker and Z. Ghahramani (eds.), volume 14. Cambridge MA: MIT Press.
9. Tibshirani, R., Hastie T., Eisen, M., Ross, D., Botstein, D., and Brown, P. (1999). Clustering methods for the analysis of DNA microarray data. Technical report, Department of Statistics, Stanford University. http://www-stat. stanford.edu/~tibs/lab/publications.html.