Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots

Min Lu, Shuaiqi Wang, Joel Lanir, Noa Fish, Yang Yue, Daniel Cohen-Or, Hui Huang

Research output: Contribution to journalArticlepeer-review


This work proposes Winglets, an enhancement to the classic scatterplot to better perceptually pronounce multiple classes by improving the perception of association and uncertainty of points to their related cluster. Designed as a pair of dual-sided strokes belonging to a data point, Winglets leverage the Gestalt principle of Closure to shape the perception of the form of the clusters, rather than use an explicit divisive encoding. Through a subtle design of two dominant attributes, length and orientation, Winglets enable viewers to perform a mental completion of the clusters. A controlled user study was conducted to examine the efficiency of Winglets in perceiving the cluster association and the uncertainty of certain points. The results show Winglets form a more prominent association of points into clusters and improve the perception of associating uncertainty.

Original languageEnglish
Article number8848845
Pages (from-to)770-779
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
StatePublished - Jan 2020
Externally publishedYes


  • Association
  • Gestalt laws
  • Scatterplot
  • Uncertainty


Dive into the research topics of 'Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots'. Together they form a unique fingerprint.

Cite this