The Community for Technology Leaders
RSS Icon
Issue No.05 - May (2011 vol.17)
pp: 584-597
Andrada Tatu , University of Konstanz, Konstanz
Georgia Albuquerque , TU Braunschweig, Braunschweig
Martin Eisemann , TU Braunschweig, Braunschweig
Peter Bak , University of Konstanz, Konstanz
Holger Theisel , University of Magdeburg, Magdeburg
Marcus Magnor , TU Braunschweig, Braunschweig
Daniel Keim , University of Konstanz, Konstanz
Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.
Dimensionality reduction, quality measures, scatterplots, parallel coordinates.
Andrada Tatu, Georgia Albuquerque, Martin Eisemann, Peter Bak, Holger Theisel, Marcus Magnor, Daniel Keim, "Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 5, pp. 584-597, May 2011, doi:10.1109/TVCG.2010.242
[1] D.A. Keim, M. Ankerst, and M. Sips, Visual Data-Mining Techniques, pp. 813-825. Kolam Publishing, 2004.
[2] J. Friedman and J. Tukey, “A Projection Pursuit Algorithm for Exploratory Data Analysis,” IEEE Trans. Computers, vol. 23, no. 9, pp. 881-890, Sept. 1974.
[3] P.J. Huber, “Projection Pursuit,” The Annals of Statistics, vol. 13, no. 2, pp. 435-475, 1985.
[4] D. Asimov, “The Grand Tour: A Tool for Viewing Multidimensional Data,” J. Scientific and Statistical Computing, vol. 6, no. 1, pp. 128-143, 1985.
[5] D. Cook, A. Buja, J. Cabreta, and C. Hurley, “Grand Tour and Projection Pursuit,” J. Computational and Statistical Computing, vol. 4, no. 3, pp. 155-172, 1995.
[6] M.A. Fisherkeller, J.H. Friedman, and J.W. Tukey, Prim-9: An Interactive Multi-Dimensional Data Display and Analysis System, W.S. Sleveland, ed. Chapman and Hall, 1987.
[7] J. Tukey and P. Tukey, “Computing Graphics and Exploratory Data Analysis: An Introduction,” Proc. Sixth Ann. Conf. and Exposition Computer Graphics, 1985.
[8] L. Wilkinson, A. Anand, and R. Grossman, “Graph-Theoretic Scagnostics,” Proc. IEEE Symp. Information Visualization, pp. 157-164, 2005.
[9] Y. Koren and L. Carmel, “Visualization of Labeled Data Using Linear Transformations,” Proc. IEEE Symp. Information Visualization, p. 16, 2003.
[10] A. Inselberg, “The Plane with Parallel Coordinates,” The Visual Computer, vol. 1, no. 4, pp. 69-91, Dec. 1985.
[11] M.O. Ward, “Xmdvtool: Integrating Multiple Methods for Visualizing Multivariate Data,” Proc. IEEE Symp. Information Visualization, pp. 326-333, 1994.
[12] D. Guo, J. Chen, A.M. MacEachren, and K. Liao, “A Visualization System for Space-Time and Multivariate Patterns (Vis-Stamp),” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 6, pp. 1461-1474, Nov. 2006.
[13] M. Ankerst, S. Berchtold, and D.A. Keim, “Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data,” Proc. IEEE Symp. Information Visualization, 1998.
[14] J. Yang, M. Ward, E. Rundensteiner, and S. Huang, “Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets,” , 2003.
[15] D. Guo, “Coordinating Computational and Visual Approaches for Interactive Feature Selection and Multivariate Clustering,” Information Visualization, vol. 2, no. 4, pp. 232-246, 2003.
[16] J. Schneidewind, M. Sips, and D. Keim, “Pixnostics: Towards Measuring the Value of Visualization,” Proc. IEEE Symp. Visual Analytics Science and Technology, pp. 199-206, 2006.
[17] A. Tatu, G. Albuquerque, M. Eisemann, J. Schneidewind, H. Theisel, M. Magnor, and D. Keim, “Combining Automated Analysis and Visualization Techniques for Effective Exploration of High Dimensional Data,” Proc. IEEE Symp. Visual Analytics Science and Technology, pp. 59-66, 2009.
[18] M. Sips, B. Neubert, J.P. Lewis, and P. Hanrahan, “Selecting Good Views of High-Dimensional Data Using Class Consistency,” Computer Graphics Forum, vol. 28, no. 3, pp. 831-838, 2009.
[19] S. Lloyd, “Least Squares Quantization in PCM,” IEEE Trans. Information Theory, vol. IT-28, no. 2, pp. 129-137, Mar. 1982.
[20] J.B. Macqueen, “Some Methods for Classification and Analysis of Multivariate Observations,” Proc. Fifth Berkeley Symp. Math, Statistics, and Probability, vol. 1, pp. 281-297, 1967.
[21] A.Y. Ng, M.I. Jordan, and Y. Weiss, “On Spectral Clustering: Analysis and an Algorithm,” Advances in Neural Information Processing Systems, vol. 14, pp. 849-856, MIT Press, 2001.
[22] Applied Multivariate Statistical Analysis, R.A. Johnson and D.W. Wichern, eds. Prentice-Hall, Inc., 1988.
[23] P.V.C. Hough, “Method and Means for Recognizing Complex Patterns,” US Patent 3069654, Dec. 1962.
[24] P.N. Hart, N. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Trans. Systems Science and Cybernetics, vol. SSC-4, no. 2, pp. 100-107, July 1968.
[25] D.L. Applegate, R.E. Bixby, V. Chvatal, and W.J. Cook, The Traveling Salesman Problem: A Computational Study. Princeton Univ. Press, Jan. 2007.
[26] M.A. Little, P.E. Mcsharry, S.J. Roberts, D.A.E. Costello, and I.M. Moroz, “Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection,” Biomedical Eng. Online, vol. 6, p. 23, June 2007.
[27] M.A. Little, P.E. McSharry, E.J. Hunter, and L.O. Ramig, “Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease,” IEEE Trans. Biomedical Eng., vol. 56, no. 4, pp. 1015-1022, Apr. 2009.
[28] J. Zupan, M. Novic, X. Li, and J. Gasteiger, “Classification of Multicomponent Analytical Data of Olive Oils Using Different Neural Networks,” Analytica Chimica Acta, vol. 292, pp. 219-234, 1994.
[29] W. Street, W. Wolberg, and O. Mangasarian, “Nuclear Feature Extraction for Breast Tumor Diagnosis,” Proc. IS&T/SPIE Int'l Symp. Electronic Imaging: Science and Technology, vol. 1905, pp. 861-870, 1993.
[30] S. Johansson and J. Johansson, “Interactive Dimensionality Reduction through User-Defined Combinations of Quality Metrics,” IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 6, pp. 993-1000, Nov./Dec. 2009.
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool