Visualization Symposium, IEEE Pacific (2009)
Apr. 20, 2009 to Apr. 23, 2009
Jeffrey Sukharev , University of California, Davis, USA
Chaoli Wang , University of California, Davis, USA
Kwan-Liu Ma , University of California, Davis, USA
Andrew T. Wittenberg , National Oceanic and Atmospheric Administration, USA
We present a correlation study of time-varying multivariate volumetric data sets. In most scientific disciplines, to test hypotheses and discover insights, scientists are interested in looking for connections among different variables, or among different spatial locations within a data field. In response, we propose a suite of techniques to analyze the correlations in time-varying multivariate data. Various temporal curves are utilized to organize the data and capture the temporal behaviors. To reveal patterns and find connections, we perform data clustering and segmentation using the k-means clustering and graph partitioning algorithms. We study the correlation structure of a single or a pair of variables using pointwise correlation coefficients and canonical correlation analysis. We demonstrate our approach using results on time-varying multivariate climate data sets.
J. Sukharev, C. Wang, K. Ma and A. T. Wittenberg, "Correlation study of time-varying multivariate climate data sets," 2009 IEEE Pacific Visualization Symposium(PACIFICVIS), Beijing, 2009, pp. 161-168.