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.

中文翻译：

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小翼：将多类散点图中的不确定性关联化。

这项工作提出了“小翼”，它是对经典散点图的增强，可以通过改善关联点的感知和与其相关聚类的不确定性来更好地感知多个类别。Winglets被设计为属于数据点的一对双向笔划，利用格式塔的闭包原理来形成对簇形式的感知，而不是使用显式的除法编码。通过对长度和方向这两个主要属性的微妙设计，小翼使观看者能够对群集进行心理上的完善。进行了一项受控用户研究，以检查小翼感知聚类关联的效率和某些点的不确定性。