Bibliometric Analysis On Techniques For Data Visualization - Paska Marto Hasugian; Berto Nadeak (SEAN Institute, 2024)
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.