The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Semiology of graphics
A rank-by-feature framework for interactive exploration of multidimensional data
Information Visualization
IEEE Transactions on Visualization and Computer Graphics
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the International Conference on Advanced Visual Interfaces
Techniques for precision-based visual analysis of projected data
Information Visualization - Special issue on selected papers from visualization and data analysis 2010
Multi-objective genetic programming for visual analytics
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
MusiCube: a visual music recommendation system featuring interactive evolutionary computing
Proceedings of the 2011 Visual Information Communication - International Symposium
Dual analysis of DNA microarrays
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Assisted descriptor selection based on visual comparative data analysis
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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Many visualization techniques involve mapping high-dimensional data spaces to lower-dimensional views. Unfortunately, mapping a high-dimensional data space into a scatterplot involves a loss of information; or, even worse, it can give a misleading picture of valuable structure in higher dimensions. In this paper, we propose class consistency as a measure of the quality of the mapping. Class consistency enforces the constraint that classes of n-D data are shown clearly in 2-D scatterplots. We propose two quantitative measures of class consistency, one based on the distance to the class's center of gravity, and another based on the entropies of the spatial distributions of classes. We performed an experiment where users choose good views, and show that class consistency has good precision and recall. We also evaluate both consistency measures over a range of data sets and show that these measures are efficient and robust.