A Study of Hierarchical Correlation Clustering for Scientific Volume Data

  • Yi Gu
  • Chaoli Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6455)


Correlation study is at the heart of time-varying multivariate volume data analysis and visualization. In this paper, we study hierarchical clustering of volumetric samples based on the similarity of their correlation relation. Samples are selected from a time-varying multivariate climate data set according to knowledge provided by the domain experts. We present three different hierarchical clustering methods based on quality threshold, k-means, and random walks, to investigate the correlation relation with varying levels of detail. In conjunction with qualitative clustering results integrated with volume rendering, we leverage parallel coordinates to show quantitative correlation information for a complete visualization. We also evaluate the three hierarchical clustering methods in terms of quality and performance.


Hierarchical Cluster Quality Threshold Hierarchical Cluster Algorithm Hierarchical Cluster Method Correlation Cluster 
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 Berlin Heidelberg 2010

Authors and Affiliations

  • Yi Gu
    • 1
  • Chaoli Wang
    • 1
  1. 1.Michigan Technological UniversityUSA

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