Gaining understanding of multivariate and multidimensional data through visualization

High dimensionality is a major challenge for data visualization. Parameter optimization problems require an understanding of the behaviour of the objective function in the n-dimensional space around the optimum - this is multidimensional visualization and is the traditional domain of scientific visualization. Large data tables require us to understand the relationship between attributes in the table - this is multivariate visualization and is an important aspect of information visualization. Common to both types of 'high-dimensional' visualization is a need to reduce the dimensionality for display. In this paper we present a uniform approach to the filtering of both multidimensional and multivariate data, to allow extraction of data subject to constraints on their position or value within an n-dimensional window, and on choice of dimensions for display. A simple example of understanding the trajectory of solutions from an optimization algorithm is given - this involves a combination of multidimensional and multivariate data. © 2004 Elsevier Ltd. All rights reserved.