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|a 10.1109/TVCG.2008.140
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|a eng
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| 100 |
1 |
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|a Wang, Chaoli
|e verfasserin
|4 aut
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| 245 |
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|a Importance-driven time-varying data visualization
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|c 2008
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 30.12.2008
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|a Date Revised 07.11.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a The ability to identify and present the most essential aspects of time-varying data is critically important in many areas of science and engineering. This paper introduces an importance-driven approach to time-varying volume data visualization for enhancing that ability. By conducting a block-wise analysis of the data in the joint feature-temporal space, we derive an importance curve for each data block based on the formulation of conditional entropy from information theory. Each curve characterizes the local temporal behavior of the respective block, and clustering the importance curves of all the volume blocks effectively classifies the underlying data. Based on different temporal trends exhibited by importance curves and their clustering results, we suggest several interesting and effective visualization techniques to reveal the important aspects of time-varying data
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|a Journal Article
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|a Yu, Hongfeng
|e verfasserin
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|a Ma, Kwan-Liu
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 14(2008), 6 vom: Nov., Seite 1547-54
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|x 1941-0506
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|g volume:14
|g year:2008
|g number:6
|g month:11
|g pages:1547-54
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|u http://dx.doi.org/10.1109/TVCG.2008.140
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