Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Multifield-Graphs: An Approach to Visualizing Correlations in Multifield Scalar Data
IEEE Transactions on Visualization and Computer Graphics
Visual Analysis of the Air Pollution Problem in Hong Kong
IEEE Transactions on Visualization and Computer Graphics
Visualizing Temporal Patterns in Large Multivariate Data using Modified Globbing
IEEE Transactions on Visualization and Computer Graphics
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Correlation study of time-varying multivariate climate data sets
PACIFICVIS '09 Proceedings of the 2009 IEEE Pacific Visualization Symposium
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
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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.