Graphical exploratory data analysis
Graphical exploratory data analysis
Multivariate statistical methods: a primer
Multivariate statistical methods: a primer
Designing the user interface (2nd ed.): strategies for effective human-computer interaction
Designing the user interface (2nd ed.): strategies for effective human-computer interaction
Structuring information with mental models: a tour of Boston
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Drawing graphs to convey proximity: an incremental arrangement method
ACM Transactions on Computer-Human Interaction (TOCHI)
The ecological approach to text visualization
Journal of the American Society for Information Science - Speical issue on integrating mutiple overlapping metadata standards
Information visualization: perception for design
Information visualization: perception for design
Evaluating visualizations: using a taxonomic guide
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Mapping semantic information in virtual space: dimensions, variance and individual differences
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Towards a methodology for developing visualizations
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Turning pictures into numbers: extracting and generating information from complex visualizations
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Empirical studies of information visualization: a meta-analysis
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Determining the dimensionality of multidimensional scaling representations for cognitive modeling
Journal of Mathematical Psychology
BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS
Statistics and Computing
A Bayesian Multiresolution Independence Test for Continuous Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
OZCHI '98 Proceedings of the Australasian Conference on Computer Human Interaction
A problem-oriented classification of visualization techniques
VIS '90 Proceedings of the 1st conference on Visualization '90
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies
Visualizing social network concepts
Decision Support Systems
Technical Section: An application of the V-system to the clustering of Chernoff faces
Computers and Graphics
An assessment of email and spontaneous dialog visualizations
International Journal of Human-Computer Studies
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Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using nongraphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intelligence analysis. Unfortunately, while various different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of three controlled experiments comparing the ability of four different visualization approaches to help people answer meaningful questions for binary data sets. Two of these visualizations, Chernoff faces and star glyphs, represent objects using simple icon-like displays. The other two visualizations use a spatial arrangement of the objects, based on a model of human mental representation, where more similar objects are placed nearer each other. One of these spatial displays uses a common features model of similarity, while the other uses a distinctive features model. The first experiment finds that both glyph visualizations lead to slow, inaccurate answers being given with low confidence, while the faster and more confident answers for spatial visualizations are only accurate when the common features similarity model is used. The second experiment, which considers only the spatial visualizations, supports this finding, with the common features approach again producing more accurate answers. The third experiment measures human performance using the raw data in tabular form, and so allows the usefulness of visualizations in facilitating human performance to be assessed. This experiment confirms that people are faster, more confident and more accurate when an appropriate visualization of the data is made available.