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 visual...

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Bibliographic Details
Published inComputers & graphics Vol. 28; no. 3; pp. 311 - 325
Main Authors dos Santos, Selan, Brodlie, Ken
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2004
Subjects
Multidimensional
Multivariate
Reference model
Visualization
Visualization
Multidimensional
Reference model
Multivariate
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0097-8493
1873-7684
DOI:10.1016/j.cag.2004.03.013