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Local Projective Display of Multivariate Numerical Data
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 Title & Authors
Local Projective Display of Multivariate Numerical Data
Huh, Myung-Hoe; Lee, Yong-Goo;
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 Abstract
For displaying multivariate numerical data on a 2D plane by the projection, principal components biplot and the GGobi are two main tools of data visualization. The biplot is very useful for capturing the global shape of the dataset, by representing observations and variables simultaneously on a single graph. The GGobi shows a dynamic movie of the images of observations projected onto a sequence of unit vectors floating on the -dimensional sphere. Even though these two methods are certainly very valuable, there are drawbacks. The biplot is too condensed to describe the detailed parts of the data, and the GGobi is too burdensome for ordinary data analyses. In this paper, "the local projective display(LPD)" is proposed for visualizing multivariate numerical data. Main steps of the LDP are 1) -means clustering of the data into subsets, 2) drawing principal components biplots of individual subsets, and 3) sequencing plots by Hurley`s (2004) endlink algorithm for cognitive continuity.
 Keywords
Data visualization;biplot;GGobi;supplementary data;endlink algorithm;
 Language
Korean
 Cited by
1.
움직이는 데이터 그림,허명회;

응용통계연구, 2013. vol.26. 6, pp.999-1007 crossref(new window)
1.
Moving Data Pictures, Korean Journal of Applied Statistics, 2013, 26, 6, 999  crossref(new windwow)
 References
1.
Buja, A., Cook, D. and Swayne, D. F. (1998). XGobi: Interactive Dynamic Data Visualization in the X Window System, Journal of Computational and Graphical Statistics, 7, 113-130.

2.
Choi, Y. S. (1999). Understanding Biplots and Applications (written in Korean), Busan National University Press

3.
Cook, D. and Swayne, D. F. (2007). Interactive and Dynamic Graphics for Data Analysis, Springer.

4.
Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis, Biometrika, 58, 453-467. crossref(new window)

5.
Huh, M. H. (2011). Exploratory Multivariate Data Analysis (in Korean), Freedom Academy, Korea.

6.
Hurley, C. B. (2004). Clustering visualizations of multidimensional data, Journal of Computational and Graphical Statistics, 13, 788-806. crossref(new window)