The visual display of quantitative information
The visual display of quantitative information
The need for metrics in visual information analysis
NPIV '97 Proceedings of the 1997 workshop on New paradigms in information visualization and manipulation
Metadata visualization for digital libraries: interactive timeline editing and review
Proceedings of the third ACM conference on Digital libraries
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Metrics for effective information visualization
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
An Approach to the Perceptual Optimization of Complex Visualizations
IEEE Transactions on Visualization and Computer Graphics
Give chance a chance: modeling density to enhance scatter plot quality through random data sampling
Information Visualization
Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization
Measuring Data Abstraction Quality in Multiresolution Visualizations
IEEE Transactions on Visualization and Computer Graphics
Enabling Automatic Clutter Reduction in Parallel Coordinate Plots
IEEE Transactions on Visualization and Computer Graphics
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
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 |
One of the main visual analytics characteristics is the tight integration between automatic computations and interactive visualization. This generally corresponds to the availability of powerful algorithms that allow for manipulating the data under analysis, transforming it in order to feed suitable visualizations. This paper focuses on more general purpose automatic computations and presents a methodological framework that can improve the quality of the visualizations adopted in the analytical process, using the dataset at hand and the actual visualization. In particular, the paper deals with the critical issue of visual clutter reduction, presenting a general strategy for analyzing and reducing it through random data sampling. The basic idea is to model the visualization in a virtual space in order to analyze both clutter and data features (e.g., absolute density, relative density, etc.). In this way we can measure the visual overlapping which may likely affects a visualization while representing a large dataset, obtaining precise visual quality metrics about the visualization degradation and devising automatic sampling strategies in order to improve the overall image quality. Metrics and algorithms have been tuned taking into account the results of suitable user studies. We will describe our proposal using two running case studies, one on 2D scatterplots and the other one on parallel coordinates.