Hybrid parallelization for multi-view visualization of time-dependent simulation data
EG PGV'09 Proceedings of the 9th Eurographics conference on Parallel Graphics and Visualization
Structural decomposition trees
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Towards high-dimensional data analysis in air quality research
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
Towards multifield scalar topology based on pareto optimality
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
| Hi-index | 0.00 |
The visualization and exploration of multivariate data is still a challenging task. Methods either try to visualize all variablessimultaneously at each position using glyph-based approaches or use linked views for the interaction between attribute space andphysical domain such as brushing of scatterplots. Most visualizations of the attribute space are either difficult to understand or sufferfrom visual clutter. We propose a transformation of the high-dimensional data in attribute space to 2D that results in a point cloud,called attribute cloud, such that points with similar multivariate attributes are located close to each other. The transformation isbased on ideas from multivariate density estimation and manifold learning. The resulting attribute cloud is an easy to understandvisualization of multivariate data in two dimensions. We explain several techniques to incorporate additional information into theattribute cloud, that help the user get a better understanding of multivariate data. Using different examples from fluid dynamics andclimate simulation, we show how brushing can be used to explore the attribute cloud and find interesting structures in physical space.