Association Analysis for Visual Exploration of Multivariate Scientific Data Sets

The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's mult...

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Veröffentlicht in:	IEEE transactions on visualization and computer graphics 2016-01, Vol.22 (1), p.955-964
Hauptverfasser:	Liu, Xiaotong, Shen, Han-Wei
Format:	Artikel
Sprache:	eng
Schlagworte:	Analytical models Association rules Datasets Exploration Graphical representations Information flow IP networks Isosurfaces Multivariate analysis Scalars Social network services Uniqueness Visualization
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