¹Norrköping Visualization and Interaction Studio, Linköping University, Norrköping, Sweden

Correspondence: Jimmy Johansson, Linköping University, ITN, S-601 74 Norrköping, Sweden. Tel: +46 11 363495; Fax: +46 11 363270; jimjo@itn.liu.se

Received 10 November 2005; Revised 20 December 2005; Accepted 20 January 2006; Published online 2 June 2006.

Abstract

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called 'feature animation' may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.