Abstract
Data Visualization is an important tool for tasks related to Knowledge Discovery in Databases (KDD). Often the data to be visualized is complex, have multiple dimensions or features and consists of many individual data points, making visualization with traditional icon- and pixel-based and geometric techniques difficult. In this paper we propose a combination of icon-based and geometric-based visualization techniques backed up by a Self-Organizing Map, which allows dimensionality reduction and topology preservation. The technique is applied to some datasets of simple and intermediate complexity, and the results shows that it is possible to reduce clutter and facilitate identification of associations, clusters and outliers.
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References
Keim, D.A., Kriegel, H.P.: Issues in visualizing large databases. Springer (1995)
Keim, D.A., Sips, M., Ankerst, M.: Visual data-mining techniques. Bibliothek der Universität Konstanz (2004)
De Oliveira, M.C.F., Levkowitz, H.: From visual data exploration to visual data mining: A survey. IEEE Transactions on Visualization and Computer Graphics 9(3), 378–394 (2003)
Kohonen, T.: Self-organizing maps, vol. 30. Springer (2001)
Mazza, R.: Introduction to information visualization. Springer (2009)
Ankerst, M., Grinstein, G.K.D.A.: Visual data mining: Background, techniques, and drug discovery applications. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)
Tufte, E.R., Graves-Morris, P.: The visual display of quantitative information, vol. 2. Graphics Press, Cheshire (1983)
Keim, D.A.: Visual exploration of large data sets. Communications of the ACM 44(8), 38–44 (2001)
Penn, B.S.: Using self-organizing maps to visualize high-dimensional data. Computers & Geosciences 31(5), 531–544 (2005)
Keim, D.A., Kriegel, H.P.: Visualization techniques for mining large databases: A comparison. IEEE Transactions on Knowledge and Data Engineering 8(6), 923–938 (1996)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
Vesanto, J.: Data exploration process based on the self-organizing map. PhD thesis, Helsinki University of Technology
Koua, E.: Using self-organizing maps for information visualization and knowledge discovery in complex geospatial datasets. In: Proceedings of 21st International Cartographic Renaissance (ICC), pp. 1694–1702 (2003)
Gorricha, J., Lobo, V.: Improvements on the visualization of clusters in geo-referenced data using self-organizing maps. Computers & Geosciences 43, 177–186 (2012)
Inselberg, A.: Parallel Coordinates – Visual Multidimensional Geometry and Its Applications. Springer (2009)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2), 179–188 (1936)
Garcia, J.R.M., Monteiro, A.M.V., Santos, R.D.C.: Visual data mining for identification of patterns and outliers in weather stations’ data. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 245–252. Springer, Heidelberg (2012)
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Morais, A.M.M., Quiles, M.G., Santos, R.D.C. (2014). Icon and Geometric Data Visualization with a Self-Organizing Map Grid. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_42
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DOI: https://doi.org/10.1007/978-3-319-09153-2_42
Publisher Name: Springer, Cham
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