A feature-emphasized clustering method for 2D vector field

Mengyuan Guan, Wenyao Zhang, Ning Zheng, Zhengyi Liu

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

Abstract

Large-scale vector data produce the vector field clustering in flow visualization. To emphasize essential flow features, a new clustering method for 2D vector fields is proposed in this paper. With this method, the vector field is firstly initialized as a cluster, which is then iteratively divided into a hierarchy of clusters. During the iteration, clusters are segmented with streamlines instead of straight lines. This change enables it to emphasize flow features, since streamlines are consistent with flow behaviors, and clusters shaped by streamlines are aligned to the underlying flow. It is easy to capture flow patterns and features from resulting clusters. Moreover, our method improves representative vectors of clusters, leading to a more efficient approximation to the original field. Test results show that it is superior to other similar methods in terms of preserving flow features and approximating vector fields.

Original languageEnglish
Article number6973996
Pages (from-to)729-733
Number of pages5
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2014-January
Issue numberJanuary
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: 5 Oct 20148 Oct 2014

Keywords

  • Features
  • Flow visualization
  • Vector field clustering

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