A framework for the visualization of multidimensional and multivariate data

High dimensionality is a major challenge for data visualization. Parameter optimization problems require an understanding of the behaviour of an objective function in an n-dimensional space around the optimum this is multidimensional visualization and is a natural extension of the traditional domain of scientific visualization. Large numeric data tables with observations of many attributes require us to understand the relationship between these attributes 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. Although multidimensional and multivariate data are quite distinct, we show that a common approach to dimensionality reduction is possible. This framework makes a contribution to the foundation of the data visualization field, bringing both information and scientific visualization rather closer together. To address this problem we present a uniform approach designed for both abstract and scientific data. It is based on the reduction approach, which is realized through a filtering process that allows extraction of data subject to constraints on their position or value within an n-dimensional window, and on choice of dimensions for display. The framework has been put to proof through a visualization method called HyperCell, which has been applied to several case studies. The results are presented and the system evaluated.