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Visual Exploration of Multivariate Volume Data Based on Clustering

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Scientific Visualization

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

The attribute space of a multivariate volume data set is hard to handle interactively in the context of volume visualization when more than three attributes are involved. Automatic or semi-automatic approaches such as involving clustering help to reduce the complexity of the problem. Clustering methods segment the attribute space, and the segmentation can be exploited for visual exploration of the volume data. We discuss user-guided and automatic clustering approaches of the multi-dimensional attribute space and visual representations of the results. Coordinated views of object-space volume visualization with attribute-space clustering results can be applied for interactive visual exploration of the multivariate volume data and even for interactive modification of the clustering results. Respective methods are presented and discussed and future directions are outlined.

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Author information

Authors and Affiliations

  1. Jacobs University, Bremen, Germany

    Lars Linsen

Authors
  1. Lars Linsen
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Correspondence to Lars Linsen .

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Editors and Affiliations

  1. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Charles D. Hansen

  2. e-Research Centre, University of Oxford, Oxford, United Kingdom

    Min Chen

  3. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Christopher R. Johnson

  4. Department of Computer Science, Stony Brook University, New York, New York, USA

    Arie E. Kaufman

  5. Fachbereich Informatik, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Hans Hagen

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Linsen, L. (2014). Visual Exploration of Multivariate Volume Data Based on Clustering. In: Hansen, C., Chen, M., Johnson, C., Kaufman, A., Hagen, H. (eds) Scientific Visualization. Mathematics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-6497-5_16

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