Although Parallel Sets,a popular categorical data visualization technique,intuitively reveals the frequency based relationships in details,a high-dimensional categorical dataset brings a cluttered visual display that ...Although Parallel Sets,a popular categorical data visualization technique,intuitively reveals the frequency based relationships in details,a high-dimensional categorical dataset brings a cluttered visual display that seriously obscures the relationship explorations.Association rule mining is a popular approach to discovering relationships among categorical variables.It could complement Parallel Sets to group ribbons in a meaningful way.However,it is difficult to understand a larger number of rules discovered from a high-dimensional categorical dataset.In this paper,we integrate the two approaches into a visual analytics system for exploring high-dimensional categorical data with dichotomous outcome.The system not only helps users interpret association rules intuitively,but also provides an effective dimension and category reduction approach towards a less clustered and more organized visualization.The effectiveness and efficiency of our approach are illustrated by a set of user studies and experiments with benchmark datasets.展开更多
The edge, which can encode relational data in graphs and multidimensional data in parallel coordinates plots, is an important visual primitive for encoding data in information visualization research. However, when dat...The edge, which can encode relational data in graphs and multidimensional data in parallel coordinates plots, is an important visual primitive for encoding data in information visualization research. However, when data become very large, visualizations often suffer from visual clutter as thousands of edges can easily overwhelm the display and obscure underlying patterns. Many edge-bundling techniques have been proposed to reduce visual clutter in visualizations. In this survey, we briefly introduce the visual-clutter problem in visualizations. Thereafter, we review the cost-based, geometry-based, and image-based edge-bundling methods for graphs, parallel coordinates, and flow maps. We then describe the various visualization applications that use edge-bundling techniques and discuss the evaluation studies concerning the effectiveness of edge-bundling methods. An edge-bundling taxonomy is proposed at the end of this survey.展开更多
Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the chall...Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.展开更多
文摘Although Parallel Sets,a popular categorical data visualization technique,intuitively reveals the frequency based relationships in details,a high-dimensional categorical dataset brings a cluttered visual display that seriously obscures the relationship explorations.Association rule mining is a popular approach to discovering relationships among categorical variables.It could complement Parallel Sets to group ribbons in a meaningful way.However,it is difficult to understand a larger number of rules discovered from a high-dimensional categorical dataset.In this paper,we integrate the two approaches into a visual analytics system for exploring high-dimensional categorical data with dichotomous outcome.The system not only helps users interpret association rules intuitively,but also provides an effective dimension and category reduction approach towards a less clustered and more organized visualization.The effectiveness and efficiency of our approach are illustrated by a set of user studies and experiments with benchmark datasets.
基金supported by Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (No. LYM11113)the National Natural Science Foundation of China (Nos. 61103055 and 61170204, and 61232012)
文摘The edge, which can encode relational data in graphs and multidimensional data in parallel coordinates plots, is an important visual primitive for encoding data in information visualization research. However, when data become very large, visualizations often suffer from visual clutter as thousands of edges can easily overwhelm the display and obscure underlying patterns. Many edge-bundling techniques have been proposed to reduce visual clutter in visualizations. In this survey, we briefly introduce the visual-clutter problem in visualizations. Thereafter, we review the cost-based, geometry-based, and image-based edge-bundling methods for graphs, parallel coordinates, and flow maps. We then describe the various visualization applications that use edge-bundling techniques and discuss the evaluation studies concerning the effectiveness of edge-bundling methods. An edge-bundling taxonomy is proposed at the end of this survey.
基金the Israel Science Foundation(Grant No.2366/16 and 2472/17)。
文摘Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.
