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2008

Visual Clustering in Parallel Coordinates

9 years 9 months ago
Visual Clustering in Parallel Coordinates
Parallel 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.
Hong Zhou, Xiaoru Yuan, Huamin Qu, Weiwei Cui, Bao
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where CGF
Authors Hong Zhou, Xiaoru Yuan, Huamin Qu, Weiwei Cui, Baoquan Chen
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