论文信息 - Principal component analysis of interval data: a symbolic data analysis approach

Principal component analysis of interval data: a symbolic data analysis approach

SummaryThe present paper deals with the study of continous interval data by means of suitable Principal Component Analyses (PCA). Statistical units described by interval data can be assumed as special cases of Symbolic Objects (SO) (Diday, 1987). In Symbolic Data Analysis (SDA), these data are represented as hypercubes. In the present paper, we propose some extensions of the PCA with the aim of representing, in a space of reduced dimensions, images of such hypercubes, pointing out differences and similarities according to their structural features.

Francesco Palumbo | Carlo Lauro

[1] Hans-Hermann Bock,et al. Analysis of Symbolic Data , 2000 .

[2] Edwin Diday. Introduction à l'approche symbolique en analyse des données , 1989 .

[3] E. Diday,et al. Extension de l'analyse en composantes principales à des données de type intervalle , 1997 .

[4] Francisco de Carvalho. Méthodes descriptives en analyse de données symboliques , 1992 .

[5] M. Ichino. General Metrics For Mixed Features The Cartesian Space Theory For Pattern Recognition , 1988, Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics.