Dos Santos, Selan Rodrigues (2004) A framework for the visualization of multidimensional and multivariate data. PhD thesis, University of Leeds.
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.
|Item Type:||Thesis (PhD)|
|Additional Information:||Supplied directly by the School of Computing, University of Leeds.|
|Academic Units:||The University of Leeds > Faculty of Engineering (Leeds) > School of Computing (Leeds)|
|Depositing User:||Dr L G Proll|
|Date Deposited:||03 Mar 2011 15:49|
|Last Modified:||07 Mar 2014 11:23|