Bibliometric Analysis On Techniques For Data Visualization
Keywords:
Bibliometrics, techniques, Visualization, dataAbstract
The objective of this paper is to conduct an in-depth bibliometric analysis of various techniques used in data visualization. The methodology involves collecting and analyzing bibliographies and relevant scientific publications related to data visualization techniques, specifically Multidimensional Scaling (MDS), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), T-Distributed Stochastic Neighbor Embedding (T-SNE), Tree Map (TMAP), Uniform Manifold Approximation (UMAP). The findings from this analysis are expected to provide a comprehensive overview of trends, patterns, and developments in data visualization techniques within the scholarly literature. However, this research has limitations, such as the availability of data and bibliometric methodology constraints. The social implications of an in-depth understanding of these data visualization techniques may contribute to enhancing broader understanding and application across various fields, spanning from sciences to industries. The novelty of this research lies in its comprehensive approach to bibliometric analysis, particularly focusing on data visualization techniques, and the value of this research resides in its contribution to knowledge that can serve as a foundation for further developments in the domain.
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S. Elnawawi, L. C. Siang, D. L. O’Connor, and R. B. Gopaluni, “Interactive visualization for diagnosis of industrial Model Predictive Controllers with steady-state optimizers,” Control Eng. Pract., vol. 121, Apr. 2022, doi: 10.1016/j.conengprac.2021.105056.
G. Bergk, B. Shariati, P. Safari, and J. K. Fischer, “ML-assisted QoT estimation: A dataset collection and data visualization for dataset quality evaluation,” J. Opt. Commun. Netw., vol. 14, no. 3, pp. 43–55, Mar. 2022, doi: 10.1364/JOCN.442733.
H. Chung, S. Nandhakumar, and S. Yang, “GridSet: Visualizing Individual Elements and Attributes for Analysis of Set-Typed Data,” IEEE Trans. Vis. Comput. Graph., vol. 28, no. 8, pp. 2983–2998, Aug. 2022, doi: 10.1109/TVCG.2020.3047111.
A. Boaro et al., “Visualization, navigation, augmentation. The ever-changing perspective of the neurosurgeon,” Brain and Spine, vol. 2. Elsevier BV, p. 100926, 2022. doi: 10.1016/j.bas.2022.100926.
L. M. Poste and C. F. Patterson, “Multidimensional Scaling – Sensory Analysis of Yoghurt,” Can. Inst. Food Sci. Technol. J., vol. 21, no. 3, pp. 271–278, 1988, doi: https://doi.org/10.1016/S0315-5463(88)70817-2.
T. Jiang, Y. Hou, and J. Yang, “Literature Review on the Development of Visualization Studies (2012–2022),” MDPI AG, Aug. 2023, p. 89. doi: 10.3390/engproc2023038089.
M. C. Schatz et al., “Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space,” Cell Genomics, vol. 2, no. 1. Cell Press, Jan. 12, 2022. doi: 10.1016/j.xgen.2021.100085.
K. Li, F. Wang, L. Yang, and R. Liu, “Deep feature screening: Feature selection for ultra high-dimensional data via deep neural networks.,” Neurocomputing, vol. 538, p. 126186, Jun. 2023, doi: 10.1016/j.neucom.2023.03.047.
Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” Jun. 2012, [Online]. Available: http://arxiv.org/abs/1206.5538
E. Dimara, H. Zhang, M. Tory, and S. Franconeri, “The Unmet Data Visualization Needs of Decision Makers Within Organizations,” IEEE Trans. Vis. Comput. Graph., vol. 28, no. 12, pp. 4101–4112, Dec. 2022, doi: 10.1109/TVCG.2021.3074023.
E. Sherman and L. G. Schiffman, “Quality-of-life (QOL) assessment of older consumers: A retrospective review,” J. Bus. Psychol., vol. 6, no. 1, pp. 107–119, 1991, doi: 10.1007/BF01013687.
L. M. Ränger, M. von Kurnatowski, M. Bortz, and T. Grützner, “Multi-Objective Optimization of Dividing Wall Columns and Visualization of the High-Dimensional Results,” Comput. Chem. Eng., vol. 142, Nov. 2020, doi: 10.1016/j.compchemeng.2020.107059.
Y. Zhao et al., “Metaverse: Perspectives from graphics, interactions and visualization,” Visual Informatics, vol. 6, no. 1. Elsevier B.V., pp. 56–67, Mar. 01, 2022. doi: 10.1016/j.visinf.2022.03.002.
Y. Lei et al., “Overview of structural variation calling: Simulation, identification, and visualization,” Computers in Biology and Medicine, vol. 145. Elsevier Ltd, Jun. 01, 2022. doi: 10.1016/j.compbiomed.2022.105534.
Z. Yang et al., “UAV remote sensing applications in marine monitoring: Knowledge visualization and review,” Science of the Total Environment, vol. 838. Elsevier B.V., Sep. 10, 2022. doi: 10.1016/j.scitotenv.2022.155939.
R. Ding, H. bin Dong, G. sheng Yin, J. Sun, X. dong Yu, and X. bin Feng, “An objective reduction method based on advanced clustering for many-objective optimization problems and its human-computer interaction visualization of pareto front,” Comput. Electr. Eng., vol. 93, Jul. 2021, doi: 10.1016/j.compeleceng.2021.107266.
S. Liu, D. Maljovec, B. Wang, P. T. Bremer, and V. Pascucci, “Visualizing High-Dimensional Data: Advances in the Past Decade,” IEEE Trans. Vis. Comput. Graph., vol. 23, no. 3, pp. 1249–1268, Mar. 2017, doi: 10.1109/TVCG.2016.2640960.
J. Ibrahim, M.-H. Chen, and D. Sinha, Springer Series in Statistics, vol. 27, no. 2. 2009. [Online]. Available: http://www.springerlink.com/index/D7X7KX6772HQ2135.pdf
T. T. Cai and R. Ma, “Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data,” May 2021, [Online]. Available: http://arxiv.org/abs/2105.07536
T. Suesse, A. Brenning, and V. Grupp, “Spatial linear discriminant analysis approaches for remote-sensing classification,” Spat. Stat., vol. 57, p. 100775, 2023, doi: 10.1016/j.spasta.2023.100775.
I. Stolarek, A. Samelak-Czajka, M. Figlerowicz, and P. Jackowiak, “Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data,” iScience, vol. 25, no. 10, p. 105142, 2022, doi: 10.1016/j.isci.2022.105142.
G. Belay Gebremeskel, B. Hailu, and B. Biazen, “Architecture and optimization of data mining modeling for visualization of knowledge extraction: Patient safety care,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 2, pp. 468–479, Feb. 2022, doi: 10.1016/j.jksuci.2019.12.001.
G. Jia et al., “Artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization,” Intelligent Medicine, vol. 2, no. 1. Elsevier B.V., pp. 48–53, Feb. 01, 2022. doi: 10.1016/j.imed.2021.04.001.
B. Kumar et al., “Optimization of DNS code and visualization of entrainment and mixing phenomena at cloud edges,” Parallel Comput., vol. 107, Oct. 2021, doi: 10.1016/j.parco.2021.102811.
A. Clarinval and B. Dumas, “Intra-City Traffic Data Visualization: A Systematic Literature Review,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7. Institute of Electrical and Electronics Engineers Inc., pp. 6298–6315, Jul. 01, 2022. doi: 10.1109/TITS.2021.3092036.
S. Das and N. R. Pal, “Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach; Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach,” IEEE Trans. FUZZY Syst., vol. 30, no. 7, 2022, doi: 10.1109/TFUZZ.
P. Chundury, B. Patnaik, Y. Reyazuddin, C. Tang, J. Lazar, and N. Elmqvist, “Towards Understanding Sensory Substitution for Accessible Visualization: An Interview Study,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1. IEEE Computer Society, pp. 1084–1094, Jan. 01, 2022. doi: 10.1109/TVCG.2021.3114829.
E. Adar and E. Lee-Robbins, “Roboviz: A Game-Centered Project for Information Visualization Education,” IEEE Trans. Vis. Comput. Graph., vol. 29, no. 1, pp. 268–277, Jan. 2023, doi: 10.1109/TVCG.2022.3209402.
I. Borg and P. J. F. Groenen, Modern multidimensional scaling: Theory and applications. Springer Science & Business Media, 2005.
E. Peterfreund and M. Gavish, “Multidimensional scaling of noisy high dimensional data,” Appl. Comput. Harmon. Anal., vol. 51, pp. 333–373, Mar. 2021, doi: 10.1016/j.acha.2020.11.006.
A. M. Lopes and J. A. T. Machado, “Multidimensional scaling analysis of generalized mean discrete-time fractional order controllers,” Commun. Nonlinear Sci. Numer. Simul., vol. 95, p. 105657, 2021.
D. Charalampidis, “Visualizing Population Structures by Multidimensional Scaling of Smoothed PCA-Transformed Data,” IEEE Access, vol. 11, pp. 13594–13604, 2023.
G. Dzemyda and M. Sabaliauskas, “Geometric multidimensional scaling: efficient approach for data dimensionality reduction,” J. Glob. Optim., pp. 1–25, 2022.
D. Hägele, T. Krake, and D. Weiskopf, “Uncertainty-aware multidimensional scaling,” IEEE Trans. Vis. Comput. Graph., vol. 29, no. 1, pp. 23–32, 2022.
