Visual clustering in parallel coordinates

  • Authors:

  • Hong Zhou;Xiaoru Yuan;Huamin Qu;Weiwei Cui;Baoquan Chen

  • Affiliations:

  • Computer Science & Engineering Department, The Hong Kong University of Science and Technology, Hong Kong;School of EECS & Key Laboratory of Machine Perception, Peking University, China;Computer Science & Engineering Department, The Hong Kong University of Science and Technology, Hong Kong;Computer Science & Engineering Department, The Hong Kong University of Science and Technology, Hong Kong;Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

  • Venue:
  • EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
  • Year:
  • 2008

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Abstract

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.