Correlation Study of Time-Varying Multivariate Climate Data Sets
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 point-wise correlation coefficients and canonical correlation analysis. We demonstrate our approach using results on time-varying multivariate climate data sets.
Jeffrey Sukharev Chaoli Wang Kwan-Liu Ma Andrew T.Wittenberg
University of California, Davis National Oceanic and Atmospheric Administration
国际会议
IEEE Pacific Visualization Symposium 2009(2009 IEEE太平洋可视化研讨会)
北京
英文
161-168
2009-04-29（万方平台首次上网日期，不代表论文的发表时间）