The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Analyzing high-dimensional data with motion graphics
SIAM Journal on Scientific and Statistical Computing
The CAVE: audio visual experience automatic virtual environment
Communications of the ACM
Self-organizing maps
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Visualization-Driven Structural and Statistical Analysis of Turbulent Flows
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A framework for exploring multidimensional data with 3D projections
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Hi-index | 0.00 |
Grand tour is a method for viewing multidimensional data via linear projections onto a sequence of two dimensional subspaces and then moving continuously from one projection to the next. This paper extends the method to 3D grand tour where projections are made onto three dimensional subspaces. 3D cluster-guided tour is proposed where sequences of projections are determined by cluster centroids. Cluster-guided tour makes inter-cluster distance-preserving projections under which clusters are displayed as separate as possible. Various add-on features, such as projecting variable vectors together with data points, interactive picking and drill down, and cluster similarity graphs, help further the understanding of data. A CAVE virtual reality environment is at our disposal for 3D immersive display. This approach of multidimensional visualization provides a natural metaphor to visualize clustering results and data at hand by mapping the data onto a time-indexed family of 3D natural projections suitable for human eye's exploration.