Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets

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 clu...

• Type: Conference
• Year: 2003
• Where: VISSYM
• Authors: Jing Yang, Matthew O. Ward, Elke A. Rundensteiner, Shiping Huang
• Keywords: Data Sets, Dimensionality Reduction, Lower Dimensional Spaces, VISSYM 2003, Visualization