Correlation study of time-varying multivariate climate data sets
Correlation study of time-varying multivariate climate data sets
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DOI：
10.1109/pacificvis.2009.4906852
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
Jeffrey Sukharev;Chaoli Wang;K. Ma;A. Wittenberg
中科院分区：
文献类型：
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作者：
Jeffrey Sukharev;Chaoli Wang;K. Ma;A. Wittenberg
我们提出了一个相关性研究的时变多变量体积数据集。在大多数科学学科中，为了检验假设和发现见解，科学家们有兴趣寻找不同变量之间的联系，或者数据字段中不同空间位置之间的联系。作为回应，我们提出了一套技术来分析时变多元数据的相关性。利用各种时间曲线来组织数据并捕获时间行为。为了揭示模式并找到连接，我们使用k-means聚类和图划分算法执行数据聚类和分割。我们使用逐点相关系数和典型相关分析来研究单个或成对变量的相关结构。我们证明了我们的方法使用随时间变化的多变量气候数据集的结果。
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.