Visualizing High-Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis

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 visualizat...

• Type: Journal
• Year: 2011
• Where: CGF
• Authors: Bilkis J. Ferdosi, Jos B. T. M. Roerdink
• Keywords: Astronomical Datasets, CGF 2011, Cluster Detection, Computational Geometry, Quality Criterion