Using entropy-related measures in categorical data visualization

IEEE Pacific Visualization Symposium

Volumn , Issue , 2014, Pages 81-88

  Alsakran, Jamal ^a   Huang, Xiaoke ^b   Zhao, Ye ^b   Yang, Jing ^c   Fast, Karl ^b  

^a UNIVERSITY OF JORDAN   (Jordan)

^b KENT STATE UNIVERSITY   (United States)

^c UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE   (United States)

Author keywords

Categorical data visualization; Dimension Management; Entropy; Mutual Information; Parallel Sets

Indexed keywords

DATA VISUALIZATION; VISUALIZATION;

CATEGORICAL DATA VISUALIZATION; CATEGORICAL DATASETS; CATEGORICAL VARIABLES; HIGH-DIMENSIONAL; MULTIDIMENSIONAL VISUALIZATION; MUTUAL INFORMATIONS; PARALLEL SETS; VISUAL KNOWLEDGE DISCOVERY;

ENTROPY;

EID: 84899518857     PISSN: 21658765     EISSN: 21658773     Source Type: Conference Proceeding    
DOI: 10.1109/PacificVis.2014.43     Document Type: Conference Paper

References (33)

1. G. Albuquerque, M. Eisemann, D. J. Lehmann, H. Theisel, and M. Magnor. Quality-based visualization matrices. In Proc. Vision, Modeling and Visualization (VMV) 2009, pages 341-349, Braunschweig, Germany, Nov. 2009.
2. B. Alsallakh, E. Gr ̈oller, S. Miksch, and M. Suntinger. Contingency wheel: Visual analysis of large contingency tables. In Proceedings of the International Workshop on Visual Analytics, 2011.
3. M. Ankerst, S. Berchtold, and D. A. Keim. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In Proceedings of the IEEE Symposium on Information Visualization, pages 52-60, 1998.
4. E. Bertini, A. Tatu, and D. Keim. Quality metrics in high-dimensional data visualization: An overview and systematization. IEEE Transactions on Visualization and Computer Graphics, 17(12):2203-2212.
5. C. A. Brewer. In J. Bares, editor, Color hard copy and graphic arts III, Proceedings of SPIE, volume 2171, pages 54-63, Feb. 1994.
6. M. Chen and H. Janicke. An information-theoretic framework for visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6):1206-1215, 2010.
7. W. S. Cleveland and M. E. McGill. Dynamic Graphics for Statistics. Statistics/Probability Series. Wadsworth &Brooks/Cole, 1988.
8. W. Conover. Practical nonparametric statistics. Wiley, 3th edition, 1999.
9. T. M. Cover and J. A. Thomas. Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience, 2006.
10. A. Dasgupta and R. Kosara. Pargnostics: Screen-space metrics for parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 16(6):1017-1026, Nov. 2010.
11. G. Ellis and A. Dix. Enabling automatic clutter reduction in parallel coordinate plots. IEEE Transactions on Visualization and Computer Graphics, 12(5):717-724, Sept. 2006.
12. B. J. Ferdosi and J. B. T. M. Roerdink. Visualizing high-dimensional structures by dimension ordering and filtering using subspace analysis. Comput. Graph. Forum, 30(3):1121-1130, 2011.
13. D. Filippova and B. Shneiderman. Interactive exploration of multivariate categorical data: Exploiting ranking criteria to reveal patterns and outliers. HCIL Technical Report, 2009.
14. A. Frank and A. Asuncion. UCI machine learning repository [http://archive.ics.uci.edu/ml]. University of California, Irvine, School of Information and Computer Sciences, 2010.
15. M. Friendly. Visualizing categorical data: Data, stories and pictures. SAS User Group International Conference Proceedings, pages 190-200, 1992.
16. G. D. Garson. Measures of Association. Statistical Associates Publishers, Asheboro, NC, 2012.
17. M. J. Halvey and M. T. Keane. An assessment of tag presentation techniques. In Proc. 16th International Conference on World Wide Web, pages 1313-1314, 2007.
18. A. Inselberg. Parallel Coordinates: Visual Multidimensional Geometry and Its Applications. Springer-Verlag New York, 2009.
19. S. Johansson and J. Johansson. Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics, 15:993-1000, 2009.
20. R. Kosara, F. Bendix, and H. Hauser. Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics, 12(4):558-568, 2006.
21. B. Lee, N. H. Riche, A. K. Karlson, and S. Carpendale. SparkClouds: Visualizing trends in tag clouds. IEEE Trans. Visualization and Computer Graphics, 16(6):1182-1189, 2010.
22. D. J. Lehmann, G. Albuquerque, M. Eisemann, M. Magnor, and H. Theisel. Selecting coherent and relevant plots in large scatterplot matrices. COMPUTER GRAPHICS forum, 2012.
23. J. Li, J.-B. Martens, and J. J. van Wijk. Judging correlation from scatterplots and parallel coordinate plots. Information Visualization, 9(1):13-30, Mar. 2010.
24. S.Ma and J. L. Hellerstein. Ordering categorical data to improve visualization. IEEE Information Visualization Symposium Late Breaking Hot Topics, pages 15-18, 1999.
25. T. Okada. A note on covariances for categorical data. In Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents, pages 150-157. Springer-Verlag, 2000.
26. W. Peng, M. O. Ward, and E. A. Rundensteiner. Clutter reduction in multi-dimensional data visualization using dimension reordering. In Proceedings of the IEEE Symposium on Information Visualization, pages 89-96, 2004.
27. R. Rao and S. K. Card. The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 318-322, 1994.
28. H. Riedwyl and M. Schupbach. Graphische darstellung von kontingenztafeln. Technical Report 12, Institute for Mathematical Statistics, University of Bern, June 1983.
29. A.W. Rivadeneira, D. M. Gruen, M. J. Muller, and D. R. Millen. Getting our head in the clouds: toward evaluation studies of tag clouds. In Proc. CHI'07, pages 995-998, 2007.
30. G. E. Rosario, E. A. Rundensteiner, D. C. Brown, M. O. Ward, and S. Huang. Mapping nominal values to numbers for effective visualization. Information Visualization, 3(2):80-95, June 2004.
31. J. Seo and B. Shneiderman. A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4(2):96-113, 2005.
32. Y. Xiang, D. Fuhry, R. Jin, Y. Zhao, and K. Huang. Visualizing clusters in parallel coordinates for visual knowledge discovery. In P.-N. Tan, S. Chawla, C. Ho, and J. Bailey, editors, PAKDD, volume 7301 of Lecture Notes in Computer Science, pages 505-516. Springer Berlin / Heidelberg, 2012.
33. J. Yang, W. Peng, M. O. Ward, and E. A. Rundensteiner. Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In Proceedings of the IEEE Symposium on Information Visualization, pages 105-112, 2003.