Abstract
We study in this work the properties of a new method called Gen-POIViz for data projection and visualization. It extends a radial visualization with a genetic-based optimization procedure so as to find the best possible projections. It uses as a basis a visualization called POIViz that uses Points of Interest (POIs) to display a large dataset. This visualization selected the POIs with a simple heuristic. In Gen-POIViz we have replaced this heuristic with a Genetic Algorithm (GA) which selects the best set of POIs so as to maximize an evaluation function based on the Kruskal stress. We continue in this chapter the study of Gen-POIViz by providing additional explanations and analysis of its properties. We study several possibilities in the use of a GA: we tested other layouts for the POIs (grid, any) as well as a different evaluation function. Finally, we consequently extend the experimental results to evaluate those possibilities. We found that alternative POI layout were not more efficient that the circle layout. As a conclusion, POIViz can deal with datasets that are often too large for other methods.
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Notes
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The interested reader may refer to archive.ics.uci.edu to obtain the references of each dataset and their donors.
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Bouali, F., Serres, B., Guinot, C., Venturini, G. (2022). Extending a Genetic-Based Visualization: Going Beyond the Radial Layout?. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Banissi, E. (eds) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1014. Springer, Cham. https://doi.org/10.1007/978-3-030-93119-3_21
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