Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
XmdvtoolQ:: quality-aware interactive data exploration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
IEEE Transactions on Visualization and Computer Graphics
The Concentration of Fractional Distances
IEEE Transactions on Knowledge and Data Engineering
Visualizing Diversity and Depth over a Set of Objects
IEEE Computer Graphics and Applications
Interactive visual summarization of multidimensional data
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
InfoShape: high-level views of multidimensional information
Proceedings of the 2011 Visual Information Communication - International Symposium
Structural decomposition trees
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
| Hi-index | 0.00 |
Traditional multi-dimensional visualization techniques, such as glyphs, parallel coordinates and scatterplot matrices, suffer from clutter at the display level and difficult user navigation among dimensions when visualizing high dimensional datasets. In this paper, we propose a new multi-dimensional visualization technique named a Value and Relation (VaR) display, together with a rich set of navigation and selection tools, for interactive exploration of datasets with up to hundreds of dimensions. By explicitly conveying the relationships among the dimensions of a high dimensional dataset, the VaR display helps users grasp the associations among dimensions. By using pixel-oriented techniques to present values of the data items in a condensed manner, the VaR display reveals data patterns in the dataset using as little screen space as possible. The navigation and selection tools enable users to interactively reduce clutter, navigate within the dimension space, and examine data value details within context effectively and efficiently. The VaR display scales well to datasets with large numbers of data items by employing sampling and texture mapping. A case study on a real dataset, as well as the VaR displays of multiple real datasets throughout the paper, reveals how our proposed approach helps users interactively explore high dimensional datasets with large numbers of data items.