Minimum Entropy Clustering and Applications to Gene Expression Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization
Spatialization Design: Comparing Points and Landscapes
IEEE Transactions on Visualization and Computer Graphics
Comparing Dot and Landscape Spatializations for Visual Memory Differences
IEEE Transactions on Visualization and Computer Graphics
Pargnostics: Screen-Space Metrics for Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
An Information-theoretic Framework for Visualization
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
| Hi-index | 0.00 |
A data set can be represented in any number of ways. For example, hierarchical data can be presented as a radial node-link diagram, dendrogram, force-directed layout, or tree map. Alternatively, point-observations can be shown with scatter-plots, parallel coordinates, or bar charts. Each technique has different capabilities for representing relationships. These capabilities are further modified by projection and presentation decisions within the technique category. Evaluating the many options is an essential task in visualization development. Currently, evaluation is largely based on heuristics, prior experience, and indefinable aesthetic considerations. This paper presents initial work towards an evaluation technique based in spatial autocorrelation. We find that spatial autocorrelation can be used to construct a separator between visualizations and other image types. Furthermore, this can be done with parameters amenable to interactive use and in a fashion that does not need to take plot schema characteristics as parameters.