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GLC-Frame: A Framework and Library for Exploration of Multidimensional Data with General Line Coordinates
DOI:
https://doi.org/10.24215/16666038.24.e02Keywords:
Visual Analysis, General Line Coordinates, Interactions in General Line Coordinates, Visualization of Multidimensional DataAbstract
General Line Coordinates (GLC) are a relatively new set of line-based representations for visualizing multidimensional data with the distinctive characteristics of being reversible and lossless. Given these characteristics, the GLC have a high potential for exploratory multidimensional data analysis, however only partial implementations of some of the GLC techniques are available for the visualization community. In this paper, we present the GLC-Frame, an online exploration tool that supports a dual view and allows users to upload their own dataset and interactively explore the different GLC representations without writing code. We also present the GLC-Vis Library, an open-source data visualization library supporting GLC along with traditional interactions. Finally, we provide a set of usage examples showing how the different techniques behave in both the occlusion and the cluster identification problem. In addition, we present the interactions on GLC representations using the cars dataset. Both the GLC-Frame and the GLC-Vis Library provide an exploration space that will allow the visualization community to use these new techniques and evaluate their potential.
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References
G.Dzemyda,O.Kurasova,andJ.Zilinskas,“Multidi- mensional data visualization,” Methods and applica- tions series: Springer optimization and its applications, vol. 75, no. 122, pp. 10–5555, 2013.
P. C. Wong and R. D. Bergeron, “30 years of multi- dimensional multivariate visualization,” in Scientific Visualization, Overviews, Methodologies, and Tech- niques, (USA), p. 3–33, IEEE Computer Society, 1994.
D. Borland, W. Wang, J. Zhang, J. Shrestha, and D. Gotz, “Selection bias tracking and detailed subset comparison for high-dimensional data,” IEEE Transac- tions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 429–439, 2019.
S.Liu,D.Maljovec,B.Wang,P.-T.Bremer,andV.Pas- cucci, “Visualizing high-dimensional data: Advances in the past decade,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 3, pp. 1249–1268, 2016.
G. M. Draper, Y. Livnat, and R. F. Riesenfeld, “A survey of radial methods for information visualiza- tion,” IEEE Transactions on Visualization and Com- puter Graphics, vol. 15, no. 5, pp. 759–776, 2009.
A. Inselberg, “A survey of parallel coordinates,” in Mathematical Visualization, pp. 167–179, Springer, 1998.
J. Heinrich and D. Weiskopf, “State of the art of par- allel coordinates,” in Eurographics (STARs) (M. Sbert and L. Szirmay-Kalos, eds.), pp. 95–116, 2013.
B. Kovalerchuk, Visual Knowledge Discovery and Ma- chine Learning, vol. 144. Springer Publishing Com- pany, Incorporated, 2018.
B. Kovalerchuk and V. Grishin, “Adjustable general line coordinates for visual knowledge discovery in n-d data,” Information Visualization, vol. 18, no. 1, pp. 3– 32, 2019.
L.E.Luque,M.L.Ganuza,A.S.Antonini,andS.M. Castro, “npglc-vis library for multidimensional data visualization,” in Conference on Cloud Computing, Big Data & Emerging Topics, pp. 188–202, Springer, 2021.
M. O. Ward, G. Grinstein, and D. Keim, Interactive Data Visualization: foundations, techniques, and ap- plications. CRC Press, 2010.
C.TominskiandH.Schumann,InteractiveVisualData Analysis. CRC Press, 2020.
J. H. Claessen and J. J. Van Wijk, “Flexible linked axes for multivariate data visualization,” IEEE Transac- tions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2310–2316, 2011.
B. Kovalerchuk, “Visualization of multidimensional data with collocated paired coordinates and general line coordinates,” in Visualization and Data Analysis 2014, vol. 9017, p. 90170I, International Society for Optics and Photonics, 2014.
P.T.Inc.,“Collaborativedatascience,”2015.
M. Bostock, V. Ogievetsky, and J. Heer, “D3 data- driven documents,” IEEE Transactions on Visualiza- tion and Computer Graphics, vol. 17, no. 12, pp. 2301– 2309, 2011.
W.J.Raseman,J.Jacobson,andJ.R.Kasprzyk,“Para- sol: An open source, interactive parallel coordinates library for multi-objective decision making,” Environ- mental modelling & software, vol. 116, pp. 153–163, 2019.
R. McDonald and B. Kovalerchuk, “Lossless visual knowledge discovery in high dimensional data with elliptic paired coordinates,” in 2020 24th International Conference Information Visualisation (IV), pp. 286– 291, 2020.
L. Huber, B. Kovalerchuk, and C. Recaido, “Visual knowledge discovery with general line coordinates,” arXiv preprint arXiv:2305.18429, 2023.
L.HuberandB.Kovalerchuk,“No-codeplatformfor visual knowledge discovering in general line coordi- nates: Dv 2.0,” in 2023 27th International Conference Information Visualisation (IV), pp. 316–322, IEEE, 2023.
M. Blumenschein, X. Zhang, D. Pomerenke, D. A. Keim, and J. Fuchs, “Evaluating reordering strategies for cluster identification in parallel coordinates,” Com- puter Graphics Forum, vol. 39, no. 3, pp. 537–549, 2020.
W.Peng,M.O.Ward,andE.A.Rundensteiner,“Clut- ter reduction in multi-dimensional data visualization using dimension reordering,” in IEEE Symposium on Information Visualization, pp. 89–96, IEEE, 2004.
ordering methods in parallel coordinates plots,” Jour- nal of Visual Languages & Computing, vol. 33, pp. 3– 12, 2016.
A.O.Artero,M.C.F.deOliveira,andH.Levkowitz, “Enhanced high dimensional data visualization through dimensionreductionandattributearrangement,”in
Tenth International Conference on Information Visuali- sation (IV’06), pp. 707–712, IEEE, 2006.
H. Makwana, S. Tanwani, and S. Jain, “Axes re- ordering in parallel coordinate for pattern optimiza- tion,” International Journal of Computer Applications, vol. 40, no. 13, pp. 43–48, 2012.
Preact, “Preact,” 2015. Accessed on 05.05.2022.
A.AsuncionandD.Newman,“UCImachinelearning repository,” 2007. Accessed on 2022-04-12.
A. Tyagi, T. Estro, G. Kuenning, E. Zadok, and K. Mueller, “Pc-expo: A metrics-based interactive axes reordering method for parallel coordinate displays,” IEEE Transactions on Visualization and Computer Graphics, 2022.
A. Khalid and I. I. Kamsani, “Star coordinate dimen- sion arrangement using euclidean distance and pearson correlation,” Indonesian Journal of Electrical Engi- neering and Computer Science, vol. 12, no. 1, pp. 348– 355, 2018.
M. Rubio-Sa ́nchez, D. J. Lehmann, A. Sanchez, and J.L.Rojo-A ́lvarez,“Optimalaxesfordatavalueesti- mation in star coordinates and radial axes plots,” Com- puter Graphics Forum, vol. 40, no. 3, pp. 483–494, 2021.
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2024-04-22
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Copyright (c) 2024 Leandro Luque, Antonella Antonini, María Luján Ganuza, Silvia Castro
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
[1]
“GLC-Frame: A Framework and Library for Exploration of Multidimensional Data with General Line Coordinates”, JCS&T, vol. 24, no. 1, p. e02, Apr. 2024, doi: 10.24215/16666038.24.e02.
