LAPSE:2018.0281
Published Article
LAPSE:2018.0281
RadViz Deluxe: An Attribute-Aware Display for Multivariate Data
Shenghui Cheng, Wei Xu, Klaus Mueller
July 31, 2018
Modern data, such as occurring in chemical engineering, typically entail large collections of samples with numerous dimensional components (or attributes). Visualizing the samples in relation of these components can bring valuable insight. For example, one may be able to see how a certain chemical property is expressed in the samples taken. This could reveal if there are clusters and outliers that have specific distinguishing properties. Current multivariate visualization methods lack the ability to reveal these types of information at a sufficient degree of fidelity since they are not optimized to simultaneously present the relations of the samples as well as the relations of the samples to their attributes. We propose a display that is designed to reveal these multiple relations. Our scheme is based on the concept of RadViz, but enhances the layout with three stages of iterative refinement. These refinements reduce the layout error in terms of three essential relationships—sample to sample, attribute to attribute, and sample to attribute. We demonstrate the effectiveness of our method via various real-world domain examples in the domain of chemical process engineering. In addition, we also formally derive the equivalence of RadViz to a popular multivariate interpolation method called generalized barycentric coordinates.
Keywords
generalized barycentric interpolation, multi-objective layout, multivariate data, RadViz
Suggested Citation
Cheng S, Xu W, Mueller K. RadViz Deluxe: An Attribute-Aware Display for Multivariate Data. (2018). LAPSE:2018.0281
Author Affiliations
Cheng S: Computational Science Initiative, Brookhaven National Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA; Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA
Xu W: Computational Science Initiative, Brookhaven National Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA
Mueller K: Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA
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Journal Name
Processes
Volume
5
Issue
4
Article Number
E75
Year
2017
Publication Date
2017-11-22
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr5040075, Publication Type: Journal Article
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LAPSE:2018.0281
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doi:10.3390/pr5040075
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Jul 31, 2018
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