Abstract
The analysis of data visualized with different GLCs in previous chapters shows that multiple visual features could be estimated for each individual graph. This chapter evaluates efficiency of the human visual system in discovering discriminating features for n-D data classification learning tasks in Closed Contour Paired Coordinates (traditional Stars/Radial Coordinates, and CPC Stars) in comparison with Parallel Coordinates. It is shown that Closed Contour Paired Coordinates are capable representing data in 14-D, 48-D, 96-D, 160-D, 170-D, and 192-D, where humans are capable discovering features and patterns for classification these high-dimensional data. The chapter concludes with a description of the cooperative visualization approach to enhance Knowledge Discovery in solving Data Mining/Machine Learning tasks.
All our knowledge has its origins in our perceptions.
Leonardo da Vinci.
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References
Bertini, E., Tatu, A., Keim, D.: Quality metrics in high-dimensional data visualization: an overview and systematization, IEEE Tr. Vis. Comp. Graph. 17(12), 2203–2212 (2011)
Elder, J., Goldberg, M.: Ecological statistics of Gestalt laws for the perceptual organization of contours. J. Vis. 2, 324–353 (2002). http://journalofvision.org/2/4/5/ 324
Grishin: Pictorial Analysis of Experimental Data. Nauka Publishing, Moscow, pp. 1–237 (1982)
Grishin, V., Sula, A., Ulieru, M.: Pictorial analysis: a multi-resolution data visualization approach for monitoring and diagnosis of complex systems. Int. J. Inf. Sci. 152, 1–24 (2003)
Hoffman, P.E., Grinstein, G.G.: A survey of visualizations for high-dimensional data mining. Inf. Vis. Data Min. Knowl. Discov. 47–82 (2002)
Lichman, M. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013
Trafimow, D., Marks, M. (eds.): J. Basic Appl. Soc. Psychol. 37(1), 1–2 (2015)
Trafimow, D., Rice, S.: A test of the null hypothesis significance testing procedure correlation argument. J. Gen. Psychol. 136(3), 261–270 (2009)
Valentine, J.C., Aloe, A.M., Lau, T.S.: Life after NHST: How to describe your data without “p-ing” everywhere. Basic Appl. Soc. Psychol. 3;37(5), 260–73 (2015)
Wagemans, J., Van Gool, L., Lamote, C., Foster, D.: Minimal information to determine affine shape equivalence. J. Exp. Psychol. Hum. Percept. Perform. 26(2), 443–468 (2000)
Wertheimer, M.: Gestalt theory. Social Research 11, 78–99 (1944)
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Kovalerchuk, B. (2018). Discovering Visual Features and Shape Perception Capabilities in GLC. In: Visual Knowledge Discovery and Machine Learning. Intelligent Systems Reference Library, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-73040-0_6
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DOI: https://doi.org/10.1007/978-3-319-73040-0_6
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