Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots

This work proposes <italic>Winglets</italic>, 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, <italic>Winglets</italic> leverage the Gestalt principle of <italic>Closure</italic> 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, <italic>Winglets</italic> enable viewers to perform a mental completion of the clusters. A controlled user study was conducted to examine the efficiency of <italic>Winglets</italic> in perceiving the cluster association and the uncertainty of certain points. The results show <italic>Winglets</italic> form a more prominent association of points into clusters and improve the perception of associating uncertainty.

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