A collinearity based hierarchical method to identify clusters of variables

Studies in Classification, Data Analysis, and Knowledge Organization

Volumn 0, Issue , 2005, Pages 55-62

  Laghi, Annalisa ^a   Soffritti, Gabriele ^b  

^a Regione Emilia Romagna   (Italy)

^b UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

AGGLOMERATIVE ALGORITHM; BIVARIATE CORRELATIONS; CLUSTERINGS; COLLINEARITY; CORRELATION MEASURES; HIERARCHICAL METHOD; MULTIVARIATE CORRELATION; PERFORMANCE; QUANTITATIVE VARIABLES; SIMILARITY MEASURE;

REGRESSION ANALYSIS;

EID: 85014025133     PISSN: 14318814     EISSN: 21983321     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-27373-5_7     Document Type: Conference Paper

References (9)

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4. Hartigan, J.A. (1975): Clustering Algorithms. John Wiley, New York.
5. Nicolau, F.C. and Bacelar-Nicolau, H. (1998): Some trends in the classification of variables. In: C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H. Bock and Y. Baba (Eds.): Data Science, Classification and Related Methods. Springer, Berlin, 89-98.
6. Muller, K.E. (1982): Understanding canonical correlation through the general linear model and principal components. The American Statistician, 36, 342-354.
7. Soffritti, G. (1999): Hierarchical clustering of variables: a comparison among strategies of analysis. Communications in Statistics - Simulation and Computation, 28, 977-999.
8. Soffritti, G. (2003): Identifying multiple cluster structures in a data matrix. Communications in Statistics - Simulation and Computation, 32, 1151-1177.
9. Vigneau, E. and Qannari, E.M. (2003): Clustering of variables around latent components. Communications in Statistics - Simulation and Computation, 32, 1131-1150.