Abstract
Parallel coordinates has shown itself to be a powerful method of exploring and visualizing multidimensional data. However, applying this method to large datasets often introduces clutter, resulting in reduced insight of the data under investigation. We present a new technique that combines the classical parallel coordinates plot with a synthesized dimension that uses topological proximity as an indicator of similarity. We resolve the issue of over-plotting and increase the utility of the widely-used parallel coordinates visualization.
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References
Wormbase web site WS 220 (October 2010), http://www.wormbase.org
Barlow, N., Stuart, L.: Animator: a tool for the animation of parallel coordinates. In: 8th International Conference on Information Visualization, pp. 725–730 (2004)
Cvek, U., Trutschl, M., Stone, R., Syed, Z., Clifford, J., Sabichi, A.: Multidimensional visualization tools for analysis of expression data. World Academy of Sciences, pp. 281–289 (2009)
Falkman, G.: Information visualization in clinical ontology: multidimensional analysis and interactive data exploration. A.I. In: Med., 133–158 (2001)
Fanea, E., Carpendale, C., Isenberg, T.: An interactive 3d integration of parallel coordinates and star glyphs. In: IEEE Symp. on Info. Vis., pp. 20–27 (2005)
Friendly, M., Walker, N.F.: The golden age of statistical graphics. Statistical Science 23(4), 502–535 (2008)
Fua, Y., Ward, M.O., Rundensteiner, E.A.: Hierarchical parallel coordinates for exploration of large datasets. In: IEEE Vis., pp. 43–50 (1999)
Hurley, C.: Clustering visualizations of multidimensional data. J. Comp. and Graph. Stat., 788–806 (2004)
Inselberg, A.: The plane with parallel coordinates. The Visual Computer, 69–92 (1985)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics, 59–69 (1982)
Miller, J., Wegman, E.: Construction of line densities for parallel coordinates plots. Computational Statistics and Graphics, 107–123 (1990)
Novotny, M.: Visually effective information visualization of large data. In: 8th Central European Seminar on Computer Graphics, pp. 41–48 (2004)
Peng, W., Ward, M., Rudensteiner, E.: Clutter reduction in multi-dimensional data visualization using dimension reordering. In: IEEE Symp. on Info. Vis., pp. 89–96 (2004)
Pinkston-Gosse, Kenyon, C.: DAF-16/FOXO targets genes that regulate tumor growth in Caenorhabditis elegans. Nature Genetics 39(11), 197–204 (2007)
Trutschl, M., Ryu, J., Duke, K., Reinke, V., Kim, S.: Genomewide analysis of developmental and sex-regulated gene expression profiles in caenorhabditis elegans. In: Proceedings of the National Academy of Sciences, pp. 218–223 (2001)
Trutschl, M., Dinkova, T.D., Rhoads, R.E.: Application of machine learning and visualization of heterogeneous datasets to uncover relationships between translation and development stage expression of C. elegans mRNAs. Physiological Genomics 21(2), 264–273 (2005)
Ward, M.: Xmdvtool: Integrating multiple methods for visualizing multivariate data. In: IEEE Visualization 1994, pp. 326–333 (1994)
Wegenkittl, R., Löffelmann, H., Gröller, E.: Visualizing the behavior of higher dimensional dynamic systems. In: 8th Conf. on Vis., pp. 119–125 (1997)
Wegman, E.: Hyperdimensional data analysis using parallel coordinates. J. American Stat. Assoc., 664–675 (1990)
Yang, J., Peng, W., Ward, M., Rudensteiner, E.: Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In: IEEE Symposium on Information Visualization, pp. 14–21 (2003)
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Trutschl, M., Kilgore, P.C.S.R., Cvek, U. (2013). Self-Organization in Parallel Coordinates. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_44
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DOI: https://doi.org/10.1007/978-3-642-40728-4_44
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