Column-based cluster and bar axis density in parallel coordinates

  • Authors:

  • Lei Tang;Xue-qing Li;Wen-jing Qi;Zhi-fang Jiang

  • Affiliations:

  • Shandong University, Jinan, China;Shandong University, Jinan, China;Shandong University, Jinan, China;Shandong University, Jinan, China

  • Venue:
  • Proceedings of the 3rd International Symposium on Visual Information Communication
  • Year:
  • 2010

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Abstract

In this paper we organize multi-dimensional datasets with column-based approach instead of the traditional row-based method, each column referring to one dimension and we use bar axis in place of line axis to represent corresponding dimension. Then parallel coordinates with column-based cluster, bar axis density and other techniques is used to convey a large complex multi-dimensional dataset in a relative small screen through the following steps: (a) visualization of column-based clusters with user-defined granularity to simplify the corresponding dimension where we group all the data points into several discrete values; (b) several distinct colors to distinguish the lines contain different amount of data points; (c) opacity is introduced to visualization to tell the difference among the lines with the same color; (d) brand instead of polyline to reveal the centre and the extent of each cluster; (e) layer-based drawing technique to emphasize the heavy lines and to denote the trend of multi-dimensional datasets; (f) bar axis to provide special space to illustrate the density of the dataset on each axis. Anyway, our work has two primary goals: one is to convey large dataset with legible compact vivid visualization on a limited screen area. The other one is to simultaneously reveal as many information features as possible away from clutter.