Comprehensible Visualization of Multidimensional Data: Sum of Ranking Differences-Based Parallel Coordinates

Ádám Ipkovich, Károly Héberger and János Abonyi
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Ádám Ipkovich: MTA-PE “Lendület” Complex Systems Monitoring Research Group, University of Pannonia, Egyetem u. 10, H-8200 Veszprem, Hungary
Károly Héberger: ELKH Research Centre for Natural Sciences, Institute of Excellence of the Hungarian Academy of Sciences, Magyar Tudósok Krt. 2, H-1117 Budapest, Hungary
János Abonyi: MTA-PE “Lendület” Complex Systems Monitoring Research Group, University of Pannonia, Egyetem u. 10, H-8200 Veszprem, Hungary

Mathematics, 2021, vol. 9, issue 24, 1-17

Abstract: A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.

Keywords: sum of the ranking differences; parallel coordinates; high-dimensionality data; visualization; greenhouse gas emissions; MATLAB/Octave toolbox (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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