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- W2784348580 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." @default.
- W2784348580 created "2018-02-02" @default.
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- W2784348580 date "2018-01-01" @default.
- W2784348580 modified "2023-09-27" @default.
- W2784348580 title "Discovering Visual Features and Shape Perception Capabilities in GLC" @default.
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- W2784348580 doi "https://doi.org/10.1007/978-3-319-73040-0_6" @default.
- W2784348580 hasPublicationYear "2018" @default.
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