The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
InterRing: An Interactive Tool for Visually Navigating and Manipulating Hierarchical Structures
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Image and Vision Computing
Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets
IEEE Transactions on Visualization and Computer Graphics
ACM Transactions on Knowledge Discovery from Data (TKDD)
Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
IEEE Transactions on Visualization and Computer Graphics
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Antiextensive connected operators for image and sequence processing
IEEE Transactions on Image Processing
Towards high-dimensional data analysis in air quality research
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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High-dimensional data visualization is receiving increasing interest because of the growing abundance of highdimensional datasets. To understand such datasets, visualization of the structures present in the data, such as clusters, can be an invaluable tool. Structures may be present in the full high-dimensional space, as well as in its subspaces. Two widely used methods to visualize high-dimensional data are the scatter plot matrix (SPM) and the parallel coordinate plot (PCP). SPM allows a quick overview of the structures present in pairwise combinations of dimensions. On the other hand, PCP has the potential to visualize not only bi-dimensional structures but also higher dimensional ones. A problem with SPM is that it suffers from crowding and clutter which makes interpretation hard. Approaches to reduce clutter are available in the literature, based on changing the order of the dimensions. However, usually this reordering has a high computational complexity. For effective visualization of high-dimensional structures, also PCP requires a proper ordering of the dimensions. In this paper, we propose methods for reordering dimensions in PCP in such a way that high-dimensional structures (if present) become easier to perceive. We also present a method for dimension reordering in SPM which yields results that are comparable to those of existing approaches, but at a much lower computational cost. Our approach is based on finding relevant subspaces for clustering using a quality criterion and cluster information. The quality computation and cluster detection are done in image space, using connected morphological operators. We demonstrate the potential of our approach for synthetic and astronomical datasets, and show that our method compares favorably with a number of existing approaches.