KEYWORDS: Stars, Visualization, Data modeling, Shape analysis, Feature selection, Visual analytics, Visual process modeling, Analytical research, Human vision and color perception, Statistical analysis
Although shape perception is the main information channel for brain, it has been poor used by recent visualization techniques. The difficulties of its modeling are key obstacles for visualization theory and application. Known experimental estimates of shape perception capabilities have been made for low data dimension, and they were usually not connected with data structures. More applied approach for certain data structures detection by means of shape displays are considered by the example of analytical and experimental comparison of popular now Parallel Coordinates (PCs), i.e. 2D Cartesian displays of data vectors, with polar displays known as stars. Advantages of stars vs. PCs by Gestalt Laws are shown. About twice faster feature selection and classification with stars than PCs are showed by psychological experiments for hyper-tubes structures detection in data space with dimension up to 100-200 and its subspaces. This demonstrates great reserves of visualization enhancement in comparison with many recent techniques usually focused on few data attributes analysis.
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