Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

IEEE Transactions on Visualization and Computer Graphics

Volumn 23, Issue 1, 2017, Pages 631-640

  Veras, Rafael ^a   Collins, Christopher ^a  

^a UNIVERSITY OF ONTARIO INSTITUTE OF TECHNOLOGY   (Canada)

Author keywords

antichain; data aggregation; Hierarchy data; multiscale visualization; tree cut

Indexed keywords

CLUTTER (INFORMATION THEORY); FORESTRY; VISUALIZATION;

ANTICHAIN; DATA AGGREGATION; HIERARCHY DATA; MULTISCALE VISUALIZATION; TREE CUT;

DATA VISUALIZATION;

ADULT; ARTICLE; DIRECTORY; HUMAN; PROOF OF CONCEPT;

EID: 84999143006     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2016.2598591     Document Type: Article

References (33)

1. J. Abello, F. Van Ham, and N. Krishnan. ASK-GraphView: A large scale graph visualization system. IEEE Trans. on Visual-ization and Computer Graphics, 12(5):669-676, 2006.
2. N. Andrienko and G. Andrienko. Spatial generalisation and ag-gregation of massive movement data. IEEE Trans. on Visualiza-tion and, 17(2):205-19, 2010.
3. D. Archambault and D. Greene. ThemeCrowds: Multiresolution summaries of twitter usage. International Workshop on Search and Mining User-generated Contents, pages 77-84, 2011.
4. D. Archambault, T. Munzner, and D. Auber. GrouseFlocks: Steerable exploration of graph hierarchy space. IEEE Trans. on Visualization and Computer Graphics, 14(4):900-913, 2008.
5. S. Baldassi, N. Megna, and D. C. Burr. Visual Clutter Causes High-Magnitude Errors. PLoS Biology, 4(3):e56, 2006.
6. A. Brew, D. Greene, D. Archambault, and P. Cunningham. De-riving insights from national happiness indices. Proceedings-IEEE International Conference on Data Mining, ICDM, pages 53-60, 2011.
7. M. Chuah. Dynamic aggregation with circular visual designs. In Proceedings IEEE Symposium on Information Visualization, pages 1-9, 1998.
8. C. Collins, S. Carpendale, and G. Penn. DocuBurst: Visualizing document content using language structure. Computer Graphics Forum, 28(3):1039-1046, jun 2009.
9. Q. Cui, M. Ward, E. Rundensteiner, and J. Yang. Measuring data abstraction quality in multiresolution visualizations. IEEE Trans. on Visualization and Computer Graphics, 12(5):709-716, 2006.
10. G. Ellis and A. Dix. A taxonomy of clutter reduction for informa-tion visualisation. IEEE Trans. on Visualization and Computer Graphics, 13(6):1216-1223, 2007.
11. N. Elmqvist and J.-D. Fekete. Hierarchical aggregation for infor-mation visualization: overview, techniques, and design guide-lines. IEEE Trans. on Visualization and Computer Graphics, 16(3):439-54, 2010.
12. S. Haroz and D. Whitney. How Capacity Limits of Attention In-fluence Information Visualization Effectiveness. IEEE Trans. on Visualization and Computer Graphics, 18(12):2402-2410, dec 2012.
13. D. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler. Chal-lenges in Visual Data Analysis. In Proc. of the Int. Conv. on Information Visualisation, pages 9-16. IEEE, 2006.
14. D. Koutra, U. Kang, J. Vreeken, and C. Faloutsos. Summarizing and Understanding Large Graphs. Statistical Analysis and Data Mining, 8(3):183-202, 2015.
15. R. Lamarche-perrin, Y. Demazeau, and J.-m. Vincent. Building Optimal Macroscopic Representations of Complex Multi-agent Systems. In N.-T. Nguyen, R. Kowalczyk, J. Corchado, and J. Bajo, editors, Trans. on Computational Collective Intelligence XV, volume 8670, pages 1-27. Springer Berlin Heidelberg, 2014.
16. R. Lamarche-perrin, L. M. Shnorr, J.-m. Vincent, and Y. De-mazeau. Evaluating Trace Aggregation Through Entropy Mea-sures for Optimal Performance Visualization of Large Dis-tributed Systems. Technical report, INRIA, 2012.
17. T. C. M. Lee. A Minimum Description Length Based Image Segmentation Procedure, and Its Comparison with a Cross-Validation-Based Segmentation Procedure. Journal of the Amer-ican Statistical Association, 95(1995):259-270, 1999.
18. T. C. M. Lee. An Introduction to Coding Theory and the Two-Part Minimum Description Length Principle. International Sta-tistical Review, 69(2):169-183, 2001.
19. H. Li and N. Abe. Generalizing Case Frames Using a Thesaurus and the MDL Principle. Computational linguistics, 24(2):217-244, 1998.
20. G. a. Miller. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological review, 101(2):343-352, 1956.
21. T. Munzner. Visualization Analysis & Design. CRC Press, 2014.
22. J. R. Quinlan and R. Rivest. Inferring Decision Trees Using the Minimum Description Length Principle. Information and Com-putation, 80(1989):227-248, 1989.
23. J. Rissanen. A universal prior for integers and estimation by minimum description length. Annals of Statistics, 11(2):416-431, 1983.
24. J. Rissanen. Stochastic Complexity in Statistical Inquiry. World Scientific Publishing Co., Inc., River Edge, NJ, USA, 1989.
25. R. Rosenholtz, Y. Li, Z. Jin, and J. Mansfield. Feature conges-tion: A measure of visual clutter. Journal of Vision, 6(6):827-827, 2010.
26. R. Rosenholtz, Y. Li, and L. Nakano. Measuring visual clutter. Journal of vision, 7(2):17.1-22, 2007.
27. B. Shneiderman. The eyes have it: a task by data type taxonomy for information visualizations. In Proc. 1996 IEEE Symposium on Visual Languages, pages 336-343. IEEE Comput. Soc. Press, 1996.
28. F. Töpfer and W. Pillewizer. The Principles of Selection. The Cartographic Journal, 3(1):10-16, 1966.
29. A. Wagner. Enriching a lexical semantic net with selectional preferences by means of statistical corpus analysis. In Pro-ceedings of the European Conference on Artificial Intelligence-ECAI, 2000.
30. J. M. Wolfe. Visual Search. Attention, pages 1-41, 1998.
31. A. Woodruff, J. Landay, and M. Stonebraker. Constant density visualizations of non-uniform distributions of data. In Proceed-ings of the 11th annual ACM symposium on User interface soft-ware and technology-UIST '98, pages 19-28, 1998.
32. Y. Wu, X. Liu, S. Liu, and K. L. Ma. ViSizer: A visualization resizing framework. IEEE Trans. on Visualization and Computer Graphics, 19(2):278-290, 2013.
33. B. Yost and C. North. The perceptual scalability of visualiza-tion. IEEE Trans. on Visualization and Computer Graphics, 12(5):837-844, 2006.