DNA visual and analytic data mining
VIS '97 Proceedings of the 8th conference on Visualization '97
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Visual hierarchical dimension reduction for exploration of high dimensional datasets
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Angular Brushing of Extended Parallel Coordinates
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Value and Relation Display for Interactive Exploration of High Dimensional Datasets
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Uncovering Clusters in Crowded Parallel Coordinates Visualizations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Revealing Structure within Clustered Parallel Coordinates Displays
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
The Projection Explorer: A Flexible Tool for Projection-based Multidimensional Visualization
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Topological Landscapes: A Terrain Metaphor for Scientific Data
IEEE Transactions on Visualization and Computer Graphics
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Brushing of Attribute Clouds for the Visualization of Multivariate Data
IEEE Transactions on Visualization and Computer Graphics
Glimmer: Multilevel MDS on the GPU
IEEE Transactions on Visualization and Computer Graphics
Cluster Analysis
Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
IEEE Transactions on Visualization and Computer Graphics
Scattering Points in Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
Interactive Data Visualization: Foundations, Techniques, and Applications
Interactive Data Visualization: Foundations, Techniques, and Applications
Two-Phase Mapping for Projecting Massive Data Sets
IEEE Transactions on Visualization and Computer Graphics
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
Illustrative parallel coordinates
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
Visual clustering in parallel coordinates
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
| Hi-index | 0.00 |
Researchers and analysts in modern industrial and academic environments are faced with a daunting amount of multi-dimensional data. While there has been significant development in the areas of data mining and knowledge discovery, there is still the need for improved visualizations and generic solutions. The state-of-the-art in visual analytics and exploratory data visualization is to incorporate more profound analysis methods while focusing on fast interactive abilities. The common trend in these scenarios is to either visualize an abstraction of the data set or to better utilize screen-space. This paper presents a novel technique that combines clustering, dimension reduction and multi-dimensional data representation to form a multivariate data visualization that incorporates both detail and overview. This amalgamation counters the individual drawbacks of common projection and multi-dimensional data visualization techniques, namely ambiguity and clutter. A specific clustering criterion is used to decompose a multi-dimensional data set into a hierarchical tree structure. This decomposition is embedded in a novel Dimensional Anchor visualization through the use of a weighted linear dimension reduction technique. The resulting Structural Decomposition Tree (SDT) provides not only an insight of the data set's inherent structure, but also conveys detailed coordinate value information. Further, fast and intuitive interaction techniques are explored in order to guide the user in highlighting, brushing, and filtering of the data.