The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Scatterplot matrix techniques for large N
Proceedings of the Seventeenth Symposium on the interface of computer sciences and statistics on Computer science and statistics
High-speed visual estimation using preattentive processing
ACM Transactions on Computer-Human Interaction (TOCHI)
Building perceptual textures to visualize multidimensional datasets
Proceedings of the conference on Visualization '98
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Feature congestion: a measure of display clutter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Give chance a chance: modeling density to enhance scatter plot quality through random data sampling
Information Visualization
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Information Visualization
Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
IEEE Transactions on Visualization and Computer Graphics
Visualization of labeled data using linear transformations
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
Selecting good views of high-dimensional data using class consistency
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Multi-objective genetic programming for visual analytics
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
A Taxonomy of Visual Cluster Separation Factors
Computer Graphics Forum
Selecting Coherent and Relevant Plots in Large Scatterplot Matrices
Computer Graphics Forum
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Visual quality metrics have been recently devised to automatically extract interesting visual projections out of a large number of available candidates in the exploration of high-dimensional databases. The metrics permit for instance to search within a large set of scatter plots (e.g., in a scatter plot matrix) and select the views that contain the best separation among clusters. The rationale behind these techniques is that automatic selection of "best" views is not only useful but also necessary when the number of potential projections exceeds the limit of human interpretation. While useful as a concept in general, such metrics received so far limited validation in terms of human perception. In this paper we present a perceptual study investigating the relationship between human interpretation of clusters in 2D scatter plots and the measures automatically extracted out of them. Specifically we compare a series of selected metrics and analyze how they predict human detection of clusters. A thorough discussion of results follows with reflections on their impact and directions for future research.