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The high dimensionality of data presents a major issue in understanding and interpreting the results of classification learning. Among the various approaches that address this issue, parallel coordinates visualization has proven its capabilities to enhance investigation and comprehension of data dimension features especially when the number of dimensions is high and there are numerous output classes...
A wide variety of real-world applications generate massive high dimensional categorical datasets. These datasets contain categorical variables whose values comprise a set of discrete categories. Visually exploring these datasets for insights is of great interest and importance. However, their discrete nature often confounds the direct application of existing multidimensional visualization techniques...
Understanding what causes proteins to change shape and how the resulting shape influences function will expedite the design of more narrowly focused drugs and therapies. Shape alterations are often the result of flexibility changes in a set of localized neighborhoods that may or may not act in concert. Computational models have been developed to predict flexibility changes under varying empirical...
Streamit lets users explore visualizations of text streams without prior knowledge of the data. It incorporates incoming documents from a continuous source into an existing visualization context with automatic grouping and separation based on document similarities. Streamit generates document clusters to promote better understanding. To obtain different clusterings, users can adjust the keyword importance...
Asynchronous Collaborative Visual Analytics (ACVA) leverages group sensemaking by releasing the constraints on when, where, and who works collaboratively. A significant task to be addressed before ACVA can reach its full potential is effective common ground construction, namely the process in which users evaluate insights from individual work to develop a shared understanding of insights and collectively...
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