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
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
The added value of eye tracking in the usability evaluation of a network management tool
SAICSIT '05 Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Nature-inspired visualisation of similarity and relationships in human systems and behaviours
Information Visualization - Special issue on visual analysis of human dynamics
International Journal of Human-Computer Studies
REV '07 Proceedings of the Second International Workshop on Requirements Engineering Visualization
Software process model blueprints
ICSP'10 Proceedings of the 2010 international conference on New modeling concepts for today's software processes: software process
Human-centered visualization environments
Human-centered visualization environments
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Data visualization has the potential to assist humans in analyzing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using non-graphical 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 a variety of different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of a controlled experiment comparing the ability 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. It is found 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.