Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sauber, N., Theisel, H., Seidel, H.P.: Multifield-graphs: An approach to visualizing correlations in multifield scalar data. IEEE Transactions on Visualization and Computer Graphics 12, 917–924 (2006)CrossRefGoogle Scholar
  2. 2.
    Qu, H., Chan, W.Y., Xu, A., Chung, K.L., Lau, K.H., Guo, P.: Visual analysis of the air pollution problem in Hong Kong. IEEE Transactions on Visualization and Computer Graphics 13, 1408–1415 (2007)CrossRefGoogle Scholar
  3. 3.
    Glatter, M., Huang, J., Ahern, S., Daniel, J., Lu, A.: Visualizing temporal patterns in large multivariate data using textual pattern matching. IEEE Transactions on Visualization and Computer Graphics 14, 1467–1474 (2008)CrossRefGoogle Scholar
  4. 4.
    Sukharev, J., Wang, C., Ma, K.-L., Wittenberg, A.T.: Correlation study of time-varying multivariate climate data sets. In: Proceedings of IEEE VGTC Pacific Visualization Symposium, pp. 161–168 (2009)Google Scholar
  5. 5.
    Ankerst, M., Berchtold, S., Keim, D.A.: Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In: Proceedings of IEEE Symposium on Information Visualization, pp. 52–60 (1998)Google Scholar
  6. 6.
    Yang, J., Peng, W., Ward, M.O., Rundensteiner, E.A.: Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In: Proceedings of IEEE Symposium on Information Visualization, pp. 105–112 (2003)Google Scholar
  7. 7.
    Izenman, A.J.: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, 1st edn. Springer Texts in Statistics. Springer, Heidelberg (2008)CrossRefzbMATHGoogle Scholar
  8. 8.
    Zimek, A.: Correlation Clustering. PhD thesis, Ludwig-Maximilians-Universität München (2008)Google Scholar
  9. 9.
    Pons, P., Latapy, M.: Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications 10, 191–218 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations