Axes-based visualizations with radial layouts
Proceedings of the 2004 ACM symposium on Applied computing
Finding Needles in Large-Scale Multivariate Data Haystacks
IEEE Computer Graphics and Applications
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
APVis '06 Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60
Design and Evaluation of Tiled Parallel Coordinate Visualization of Multichannel EEG Data
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Efficient reduction of access latency through object correlations in virtual environments
EURASIP Journal on Applied Signal Processing
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
Visual exploration of algorithm parameter space
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Scalable pixel based visual data exploration
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
What does the user want to see?: what do the data want to be?
Information Visualization
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
A comparison of dimensionality reduction methods using topology preservation indexes
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Profiler: integrated statistical analysis and visualization for data quality assessment
Proceedings of the International Working Conference on Advanced Visual Interfaces
Tiled parallel coordinates for the visualization of time-varying multichannel EEG data
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
Towards closing the analysis gap: visual generation of decision supporting schemes from raw data
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
Structural decomposition trees
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Interactive data mining with 3D-parallel-coordinate-trees
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Visualizing large-scale human collaboration in Wikipedia
Future Generation Computer Systems
User-driven feature space transformation
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solving this problem is dimensionality reduction. Existing dimensionality reduction techniques usually generate lower dimensional spaces that have little intuitive meaning to users and allow little user interaction. In this paper we propose a new approach to handling high dimensional data, named Visual Hierarchical Dimension Reduction (VHDR), that addresses these drawbacks. VHDR not only generates lower dimensional spaces that are meaningful to users, but also allows user interactions in most steps of the process. In VHDR, dimensions are grouped into a hierarchy, and lower dimensional spaces are constructed using clusters of the hierarchy. We have implemented the VHDR approach into XmdvTool, and extended several traditional multidimensional visualization methods to convey dimension cluster characteristics when visualizing the data set in lower dimensional spaces. Our case study of applying VHDR to a real data set supports our belief that this approach is effective in supporting the exploration of high dimensional data sets.