The Community for Technology Leaders
Visualization Symposium, IEEE Pacific (2011)
Hong Kong, China
Mar. 1, 2011 to Mar. 4, 2011
ISBN: 978-1-61284-935-5
pp: 27-34
Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.
pattern classification, data visualisation, sampling scheme, static correlation visualization, time-varying volume data, static volume classification, time-varying multivariate data sets, temporal correlation, spatial correlation, 3D time-varying multivariate data set, sampling-based approach, correlation pattern classification, volume-based correlation, sample-based correlation, Correlation, Data visualization, Meteorology, Three dimensional displays, Clustering algorithms, Atmospheric modeling, Histograms

"Static correlation visualization for large time-varying volume data," 2011 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Hong Kong, 2011, pp. 27-34.
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