Proceedings of the conference on Visualization '98
Information visualization: perception for design
Information visualization: perception for design
Hierarchical Splatting of Scattered Data
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Information Visualization
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CMV '05 Proceedings of the Coordinated and Multiple Views in Exploratory Visualization
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
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IEEE Transactions on Visualization and Computer Graphics
Applying Animation to the Visual Analysis of Financial Time-Dependent Data
IV '07 Proceedings of the 11th International Conference Information Visualization
Trajectory-based visual analysis of large financial time series data
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IEEE Computer Graphics and Applications
Effectiveness of Animation in Trend Visualization
IEEE Transactions on Visualization and Computer Graphics
GPU-accelerated 2D point cloud visualization using smooth splines for visual analytics applications
Proceedings of the 24th Spring Conference on Computer Graphics
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Expert Systems with Applications: An International Journal
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Two dimensional point data can be considered one of the most basic, yet one of the most ubiquitous data types arising in a wide variety of applications. The basic scatter plot approach is widely used and highly effective for data sets of small to moderate size. However, it shows scalability problems for data sets of increasing size, of multiple classes and of time-dependency. In this short paper, we therefore present an improved visual analysis of such point clouds. The basic idea is to monitor certain statistical properties of the data for each point and for each class as a function of time. The output of the statistic analysis is used for identification of interesting data views decreasing information overload. The data is interactively visualized using various techniques. In this paper, we specify the problem, detail our approach, and present application results based on a real world data set.