Visual Clustering in Parallel Coordinates

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dc.contributor.authorZhou, Hongen_US
dc.contributor.authorYuan, Xiaoruen_US
dc.contributor.authorQu, Huaminen_US
dc.contributor.authorCui, Weiweien_US
dc.contributor.authorChen, Baoquanen_US
dc.contributor.editorA. Vilanova, A. Telea, G. Scheuermann, and T. Moelleren_US
dc.date.accessioned2014-02-21T18:45:18Z
dc.date.available2014-02-21T18:45:18Z
dc.date.issued2008en_US
dc.description.abstractParallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume27en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2008.01241.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleVisual Clustering in Parallel Coordinatesen_US
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27-Issue 3
EuroVis08: Joint Eurographics - IEEE VGTC Symposium on Visualization