Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots.
IEEE transactions on visualization and computer graphics(2020)
摘要
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.
更多查看译文
关键词
Visualization,Uncertainty,Image color analysis,Shape,Clustering algorithms,Encoding
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络