Properties of normalized radial visualizations

Information Visualization

Volumn 11, Issue 4, 2012, Pages 273-300

  Daniels, Karen ^a   Grinstein, Georges ^a   Russell, Adam ^a   Glidden, Mason ^b  

^a UNIVERSITY OF MASSACHUSETTS LOWELL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Cluster analysis; Projections; Radial visualization; Visual analytics; Visualization

Indexed keywords

AFFINE TRANSFORMATIONS; CLUSTER VISUALIZATION; NORMALIZED RADIAL VISUALIZATIONS; PARALLEL COORDINATES; PROJECTIONS; PROJECTIVE TRANSFORMATION; RADIAL VISUALIZATION; VISUAL ANALYTICS;

ANCHORS; CLUSTER ANALYSIS; DATA VISUALIZATION; FLOW VISUALIZATION; SET THEORY;

VISUALIZATION;

EID: 84875753409     PISSN: 14738716     EISSN: 14738724     Source Type: Journal    
DOI: 10.1177/1473871612439357     Document Type: Article

References (55)

1. Hoffman P, Grinstein G, Marx K, et al. DNA visual and analytic data mining. In: VIS '97: proceedings of the 8th conference on visualization '97. Los Alamitos, CA: IEEE Computer Society Press, 1997, pp.437-441.
2. Olsen KA, Korfhage RR, Sochats KM, et al. Visualisation of a document collection: the vibe system. Inform Process Manag 1993; 29(1): 69-81.
3. Inselberg A. Parallel coordinates: visual multidimensional geometry and its applications. New York: Springer, 2009.
4. d'Ocagne M. Coordonnées Parallèles et Axiales: Méthode de transformation géométrique et procédé nouveau de calcul graphique déduits de la considération des coordonnées parallè ls. Paris: Gauthier-Villars, 1885.
5. Moustafa R and Wegman E. On some generalization of parallel coordinate plots. Fairfax, VA: George Mason University, 2002.
6. Grinstein G, Jessee CB, Hoffman P, et al. Highdimensional visualization support for data mining gene expression data. In: Grigorenko E (ed.) DNA arrays, technology, and experimental strategies. New York: CRC Press, 2002, pp.86-131.
7. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531-537.
8. Ward M, Grinstein G and Keim D. Interactive data visualization: foundations, techniques, and applications. A.K. Peters Ltd, 2010.
9. Ankerst M, Keim D and Kriegel HP. Circle segments: a technique for visually exploring large multidimensional data sets. Hum Factors 1996; 1501: 5-8.
10. Sharko J, Grinstein G and Marx KA. Vectorized Radviz and its application to multiple cluster datasets. IEEE T Vis Comput Gr 2008; 14: 1444-1451.
11. Draper GM, Livnat Y and Riesenfeld RF. A survey of radial methods for information visualization. IEEE T Vis Comput Gr 2009; 14(5): 759-776.
12. Newman DJ, Hettich S, Blake CL, et al. UCI Repository of machine learning databases. University of California, Irvine, Department of Information and Computer Sciences, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html
13. Hoffman PE. Table visualizations: a formal model and its applications. PhD Thesis. University of Massachusetts Lowell, MA, 1999.
14. Dracheva T, Philip R, Xiao W, et al. Distinguishing lung tumours from normal lung based on a small set of genes. Lung Cancer 2007; 55(2): 157-164.
15. Grinstein G, TrutschlMand Cvek U. High-dimensional visualizations. In: Proceedings of the visual data mining workshop. ACM Press. New York, NY., 2001 pp.7-19.
16. Grinstein GG, Hoffman PE and Pickett RM. Benchmark development for the evaluation of visualization for data mining. In: Fayyad U, Grinstein G and Wierse A (eds) Information visualization in data mining and knowledge discovery. San Francisco, CA: Morgan Kaufmann Publishers Inc., 2002, pp.129-176.
17. Ankerst M, Berchtold S and Keim D. Similarity clustering of dimensions for enhanced visualization of multidimensional data. In: Wills G and Dill J (eds) Proceedings of the IEEE symposium on information visualization. IEEE Computer Society Press, 1998, pp.52-60.
18. Nováková L. Visualization data for data mining. PhD Thesis, Czech Technical University, Prague, 2009.
19. Kandogan E. Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In: KDD '01: proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM, 2001, pp.107-116.
20. Kandogan E. Star coordinates: a multi-dimensional visualization technique with uniform treatment of dimensions. In: Proceedings of the 7th ACM international conference on knowledge discovery and data mining. IEEE Computer Society, 2000, pp.107-116.
21. Bordignon AL, Castro R, Lopes H, et al. Exploratory visualization based on multidimensional transfer functions and star coordinates. In: Brazilian symposium on computer graphics and image processing. IEEE Computer Society, 2006, pp.273-280.
22. Soldati M, Doulis M and Csillaghy A. SphereViz - data exploration in a virtual reality environment. In: International conference on information visualisation. 2007, pp.680-683.
23. Cveka U, Trutschl M, Stone II R, et al. Multidimensional visualization tools for analysis of expression data. In: International conference on bioinformatics and computational biology, world academy of science, engineering and technology (vol. 54). 2009, pp.281-289.
24. Artero AO and de Oliveira MCF. Viz3D: effective exploratory visualization of large multidimensional data sets. In: SIBGRAPI '04: proceedings of the computer graphics and image processing, XVII Brazilian symposium. Washington, DC: IEEE Computer Society, 2004, pp.340-347.
25. de Oliveira MCF and Levkowitz H. From visual data exploration to visual data mining: a survey. IEEE T Vis Comput Gr 2003; 9(3): 378-394.
26. Stasko J and Zhang E. Focus+Context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. In: IEEE symposium on information visualization, 2000, p. 57.
27. Zhang A. Advanced analysis of gene expression microarray data. Hackensack, NJ: World Scientific Publishing, 2006.
28. Konecni S, Zhou J and Grinstein G. A visual analytics model applied to lead generation library design in drug discovery. In: IV '09: proceedings of the 2009 13th international conference information visualisation. Washington, DC: IEEE Computer Society, 2009, pp.345-352.
29. Lalanne D, Bertini E, Hertzog P, et al. Visual analysis of corporate network intelligence: abstracting and reasoning on yesterdays for acting today. In: VizSEC 2007: mathematics and visualization. Berlin: Springer, 2008, pp.115-130.
30. Suh C, Sieg SC, Heying MJ, et al. Visualization of highdimensional combinatorial catalysis data. J Comb Chem 2009; 11(3): 385-392.
31. Brown M, Grundy W, Lin D, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci 2000; 97(1): 262-267.
32. Bhasi K, Zhang L, Brazeau AD, et al. VizStruct for visualization of genome-wide SNP analyses. Bioinformatics 2006; 22(13): 1569-1576.
33. Nováková L and Štêpánková O. Visualization of trends using RadViz. Berlin: Springer, 2009.
34. Zschimmer AJ and Roberts JC. Bifocal radial visualization of intranet search results using image caching. In: Proceedings of the fifth theory and practice of computer graphics. 2007, pp.115-122.
35. DiCaro L, Frias-Martinez V and Frias-Martinez E. Analyzing the role of dimension arrangement for data visualization in Radviz. In: 14th Pacific-Asia conference, PAKDD 2010, Part II (LNCS). 2010, pp.125-132.
36. Peng W, Ward MO and Rundensteiner EA. Clutter reduction in multi-dimensional data visualization using dimension reordering. In: INFOVIS '04: proceedings of the IEEE symposium on information visualization. Washington, DC: IEEE Computer Society, 2004. pp.89-96.
37. Bertini E, Aquila LD and Santucci G. SpringView: cooperation of Radviz and parallel coordinates for view optimization and clutter reduction. In: International conference on coordinated and multiple views in exploratory visualization. 2005, pp.22-29.
38. Foley J, Dam AV, Feiner S, et al. Computer graphics: principles and practice. 2nd ed. Reading, MA: Addison Wesley, 1990.
39. Wyszecki G. On projective transformations of the CIE chromaticity diagram. J Opt Soc Am 1956; 46(11): 982-986.
40. Bouman CA. Digital image processing notes: chromaticity coordinates. Purdue University, 2010. https://engineering.purdue.edu/~bouman/ece637/notes/ pdf/ColorSpaces.pdf
41. Yaglom IM. Geometric transformations III (translated from Russian by Abe Senitzer). New York: Random House, 1973.
42. Farin G. Curves and surfaces for CAGD: a practical guide. 5th ed. Morgan Kaufmann, 2002.
43. O'Rourke J. Computational geometry in C. Cambridge: Cambridge University Press, 1999.
44. Hodge W and Pedoe D. Methods of algebraic geometry. Cambridge: Cambridge University Press, 1947.
45. Birchfield S. An introduction to projective geometry (for computer vision). 1998. vision.stanford.edu/~birch/projective/
46. Room TG. A background (natural, synthetic and algebraic) to geometry. Cambridge: Cambridge University Press, 1967.
47. Carlblom I and Paciorek J. Planar geometric projections and viewing transformations. ACM Comput Surv 1978; 10(4): 465-502.
48. Eberly D. Perspective projection of an ellipsoid, www.geometrictools.com/ Documentation/PerspectiveProjectionEllipsoid.pdf (1998).
49. Tyler C. Rules of perspective, http://www.ski.org/CWTyler-lab/CWTyler/ ArtInvestigations/PerspectiveRules/PerspectiveRules.html (2010).
50. Kayikcioglu T, Gangal A and Ozer M. Reconstructing ellipsoids from three projection contours. Pattern Recognition Letters 2000; 21: 959-968.
51. Williams R. Geometry of navigation. Horwood Publishing Limited, 1998.
52. Bellman R. Adaptive control processes: a guided tour. Princeton University Press, 1961.
53. Hocking RR. Methods and applications of linear models: regression and the analysis of variance. New York: Wiley, 1996.
54. Weisstein EW. Positive definite matrix, from mathworld - a wolfram web resource, http://mathworld.wolfram.- com/PositiveDefiniteMatrix.html (2011).
55. Meyer CD. (ed.) Matrix analysis and applied linear algebra. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2000.