A new axes Re-ordering method in parallel coordinates visualization

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 2, Issue , 2012, Pages 252-257

  Lu, Liang Fu ^a   Huang, Mao Lin ^a   Huang, Tze Haw ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

axes re ordering; Multidimensional data visualization; nonlinear correlation coefficient; parallel coordinates; singular value decomposition; visual analytics

Indexed keywords

MULTI-DIMENSIONAL DATA VISUALIZATION; NON-LINEAR CORRELATIONS; PARALLEL COORDINATES; RE ORDERINGS; VISUAL ANALYTICS;

COMPUTATIONAL COMPLEXITY; LEARNING SYSTEMS; OPTIMIZATION; SINGULAR VALUE DECOMPOSITION; VISUALIZATION;

DATA VISUALIZATION;

EID: 84873592880     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.148     Document Type: Conference Paper

References (30)

1. Johansson, S. and J. Johansson, Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics. IEEE Transactions on Visualization and Computer Graphics, 2009. 15(6): p. 993-1000.
2. Ankerst, M., S. Berchtold, and D.A. Keim. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. Proceedings of IEEE Symposium on Information Visualization, 1998:p. 52-60.
3. Inselberg, A., The plane with parallel coordinates. The Visual Computer, 1985. 1(2): p. 69-91.
4. Wegman, E.J., Hyperdimensional Data Analysis Using Parallel Coordinates. Journal of the American Statistical Association, 1990. 85(411): p. 664-675.
5. Becker, R.A. and W.S. Cleveland, Brushing Scatterplots. Technometrics, 1987. 29(2): p. 127-142.
6. Rao, R. and S. Card. The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. Proceedings of the SIGCHI conference on Human factors in computing systems. 1994. Boston, MA, USA: ACM.p.318-322.
7. Keim, D.A., Designing pixel-oriented visualization techniques: theory and applications. IEEE Transactions on Visualization and Computer Graphics, 2000. 6(1): p. 59-78. (Pubitemid 30594716)
8. Bertini, E., A. Tatu, and D. Keim, Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization. IEEE Transactions on Visualization and Computer Graphics, 2011. 17(12): p. 2203-2212.
9. Dasgupta, A. and R. Kosara, Pargnostics: Screen-Space Metrics for Parallel Coordinates. IEEE Transactions on Visualization and Computer Graphics, 2010. 16(6): p. 1017-1026.
10. Tatu, A.,Albuquerque, G.,Eisemann, M.,Schneidewind, J.,Theisel, H. Magnork, M.,Keim, D. Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. Proceedings of IEEE Symposium on Visual Analytics Science and Technology, 2009, VAST 2009. p. 59-66.
11. Tatu, A.,Albuquerque, G.,Eisemann, M.,Bak, P.,Theisel, H.Magnork, M.,Keim, D. Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics, 2011. 17(5): p. 584-597.
12. J.Bertin, Semiology of graphics. 1983: University of Wisconsin Press.
13. Hahsler, M., K. Hornik, and C. Buchta, Getting things in order : an introduction to the {R} package seriation. Journal of Statistical Software, 2008. 25(3):p. 1-34.
14. Hurley, C.B. and R.W. Oldford, Pairwise Display of High-Dimensional Information via Eulerian Tours and Hamiltonian Decompositions. Journal of Computational and Graphical Statistics, 2010. 19(4): p. 861-886.
15. Peng, W., M.O. Ward, and E.A. Rundensteiner, Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering, Proceedings of the IEEE Symposium on Information Visualization. 2004, IEEE Computer Society. p. 89-96. (Pubitemid 40449132)
16. Artero, A.O., M.C.F.D. Oliveira, and H. Levkowitz, Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement, Proceedings of the conference on Information Visualization. 2006, IEEE Computer Society. p. 707-712.
17. Friendly, M. and E. Kwan, Effect ordering for data displays. Computational Statistics & Data Analysis, 2003. 43(4): p. 509-539. (Pubitemid 36990918)
18. J. Yang, M.O.W., E.A. Rundensteiner and S. Huang, Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets, in Joint EUROGRAPHICS-IEEE TCVG Symposium on Visualization (2003), S.H. G.-P. Bonneau, C. D. Hansen, Editor. 2003. p. 19-28.
19. Guo, D., Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Information Visualization, 2003. 2(4): p. 232-246.
20. Albuquerque, G., et al. Improving the visual analysis of highdimensional datasets using quality measures. Proceedings of IEEE Symposium on Visual Analytics Science and Technology (VAST). 2010:p.19-26.
21. Golub, G.H. and C.F.V. Loan, Matrix Computations. 1983, Baltimore: John Hopkins University Press.
22. Rodgers, J. and A. Nicewander, Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 1988. 42(1): p. 59-66.
23. Zheng Rong, Y. and M. Zwolinski, Mutual information theory for adaptive mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. 23(4): p. 396-403. (Pubitemid 32416303)
24. Matsuda, H., Physical nature of higher-order mutual information: Intrinsic correlations and frustration. Physical Review E, 2000. 62(3): p. 3096-3102.
25. Drmota, M. and W. Szpankowski, Precise minimax redundancy and regret. IEEE Transactions on Information Theory, 2004. 50(11): p. 2686-2707.
26. Zhiyuan, S., W. Qiang, and S. Yi. Effects of statistical distribution on nonlinear correlation coefficient. Proceedings of IEEE Instrumentation and Measurement Technology Conference (I2MTC), 2011. 2011.
27. Wang, Q., Y. Shen, and J.Q. Zhang, A nonlinear correlation measure for multivariable data set. Physica D: Nonlinear Phenomena, 2005. 200(3-4): p. 287-295. (Pubitemid 40114470)
28. Zhuang Chu Qiang, W.Y.S., Mathematical Statistics with Applications. 1992, Guangzhou: South China science and technology university press.
29. University of California, Irvine. Center for Machine Learning and Intelligent Systems: http://archive.ics.uci.edu/ml/datasets.html.
30. W. H. M. L. Huang, "Exploring the Relative Importance of Number of Edge Crossings and Size of Crossing Angle: A Quantitative Perspective," International Journal of Advanced Intelligence, 2011.3(1), p. 25-42.