Visual Exploration of Multivariate Volume Data Based on Clustering

  • Lars Linsen
Part of the Mathematics and Visualization book series (MATHVISUAL)


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.


Independent Component Analysis Attribute Space Cluster Result Object Space Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Jacobs UniversityBremenGermany

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