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.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
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)
Izenman, A.J.: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, 1st edn. Springer Texts in Statistics. Springer, Heidelberg (2008)
Zimek, A.: Correlation Clustering. PhD thesis, Ludwig-Maximilians-Universität München (2008)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications 10, 191–218 (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, Y., Wang, C. (2010). A Study of Hierarchical Correlation Clustering for Scientific Volume Data. In: , et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-17277-9_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
eBook Packages: Computer ScienceComputer Science (R0)