The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Scatterplot matrix techniques for large N
Proceedings of the Seventeenth Symposium on the interface of computer sciences and statistics on Computer science and statistics
Technometrics
Choosing effective colours for data visualization
Proceedings of the 7th conference on Visualization '96
DNA visual and analytic data mining
VIS '97 Proceedings of the 8th conference on Visualization '97
Visualizing Data
Designing Pixel-Oriented Visualization Techniques: Theory and Applications
IEEE Transactions on Visualization and Computer Graphics
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
A rank-by-feature framework for interactive exploration of multidimensional data
Information Visualization
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the International Conference on Advanced Visual Interfaces
IEEE Transactions on Visualization and Computer Graphics
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
See what you know: analyzing data distribution to improve density map visualization
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Hi-index | 0.00 |
The scatterplot matrix (SPLOM) is a well-established technique to visually explore high-dimensional data sets. It is characterized by the number of scatterplots (plots) of which it consists of. Unfortunately, this number quadratically grows with the number of the data set’s dimensions. Thus, an SPLOM scales very poorly. Consequently, the usefulness of SPLOMs is restricted to a small number of dimensions. For this, several approaches already exist to explore such ‘small’ SPLOMs. Those approaches address the scalability problem just indirectly and without solving it. Therefore, we introduce a new greedy approach to manage ‘large’ SPLOMs with more than 100 dimensions. We establish a combined visualization and interaction scheme that produces intuitively interpretable SPLOMs by combining known quality measures, a pre-process reordering and a perception-based abstraction. With this scheme, the user can interactively find large amounts of relevant plots in large SPLOMs. © 2012 Wiley Periodicals, Inc.