Abstract
Visualization is an important approach to analyzing high dimensional datasets, which are common in important applications such as financial analytics, multimedia analysis, and genomic analysis. However, larger numbers of dimensions in high dimensional datasets not only cause visual clutter in the display, but also cause difficult user navigation among dimensions. To overcome these problems, dimension management, such as subspace construction, dimension ordering and spacing, and multivariate relationship examination, needs to be provided in high dimensional visualization systems. In this book chapter, we propose a general framework for dimension management in high dimensional visualization that provides a guideline for the design and development of dimension management functions in high dimensional visualization systems. We then present our recent work on dimension management in high dimensional visualization, namely the Hierarchical Dimension Management approach, the Value and Relation display, and the Multivariate Visual Explanation approach, as examples to illustrate the proposed framework.
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Yang, J., Barlowe, S. (2009). A Dimension Management Framework for High Dimensional Visualization. In: Ras, Z.W., Ribarsky, W. (eds) Advances in Information and Intelligent Systems. Studies in Computational Intelligence, vol 251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04141-9_13
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DOI: https://doi.org/10.1007/978-3-642-04141-9_13
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