High-dimensional Scatterplot Matrices
High-Dimensional Scatterplot Matrices is a visualization framework that extends traditional scatterplot matrices to enable the interactive exploration and analysis of pairwise relationships among variables in high-dimensional datasets by systematically arranging bivariate scatterplots in a grid structure while incorporating dimensionality reduction, variable ordering, and scalability techniques to mitigate visual clutter and enhance interpretability.
18
Publications
1.1K
Citations
47
Authors
29
Institutions
Publications per year
2017–2026
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Exploring high-D spaces with multiform matrices and small multiples
Alan M. MacEachren, D. Xiping, Franklin A. Hardisty et al. · 2004 · 76 citations
ScagExplorer: Exploring Scatterplots by Their Scagnostics
Leland Wilkinson · 2014 · 73 citations
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University of Vienna
Vienna, Austria
2 h-index
2 publications
204 citations

Worcester Polytechnic Institute
Worcester, United States
2 h-index
12 publications
462 citations
University Hospital Magdeburg
Magdeburg, Germany
2 h-index
4 publications
220 citations
University of Illinois Chicago
Chicago, United States
2 h-index
4 publications
214 citations
Technische Universität Braunschweig
Braunschweig, Germany
2 h-index
6 publications
330 citations

Ghent University
Ghent, Belgium
1 h-index
1 publications
21 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
168 citations
The University of Osaka
Osaka, Japan
1 h-index
1 publications
12 citations
1–8 of 29
2 publications · 69 citations
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TVCG · 2 publications · 217 citations
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2 publications · 232 citations
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1 publications · 11 citations
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Conference · 1 publications · 30 citations
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