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ComVisMD—Compact 2D Visualization of Multidimensional Data: Experimenting with Two Different Datasets

Abstract : Interpreting data with many attributes is a difficult issue. A simple 2D display, projecting two attributes onto two dimensions, is relatively easy to interpret but provides limited help to see multidimensional correlations. We propose a tool, ComVisMD, which displays, from a dataset, five dimensions in compact 2D maps. A map contains cells; each one represents an object from the dataset. In addition to the usual horizontal and vertical projections and the use of colors, we offer holes and shapes. In order to compact the display, we partition objects according to two dimensions, grouping values of each dimension into up to seven categories. In this paper, we present two case studies covering two different domains, a cricket player dataset and a heart disease dataset. The cricket dataset has 15 attributes and 2170 objects. We show how, using ComVisMD, correlations between variables can be found in an intuitive way. The heart disease dataset has 14 attributes and 297 objects. Blokh and Stambler, in the June 2015 issue of “Aging and Disease,” state that individual attributes show little correlation with heart disease. Yet in combination the correlation improves dramatically. We show how ComVisMD helps visualize those multidimensional correlations between four attributes and heart disease diagnosis.
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https://hal.archives-ouvertes.fr/hal-03131685
Contributor : Mireille Ducassé <>
Submitted on : Thursday, February 4, 2021 - 3:34:22 PM
Last modification on : Saturday, February 6, 2021 - 3:26:34 AM

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  • HAL Id : hal-03131685, version 1

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Shridhar Dandin, Mireille Ducassé. ComVisMD—Compact 2D Visualization of Multidimensional Data: Experimenting with Two Different Datasets. H. Sharma et al. Intelligent Learning for Computer Vision, 61, Springer Nature Singapore Pte Ltd, 2021, Lecture Notes on Data Engineering and Communications Technologies. ⟨hal-03131685⟩

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