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.
|Number of pages||10|
|Journal||IEEE Transactions on Visualization and Computer Graphics|
|State||Published - Jan 2020|
Bibliographical noteFunding Information:
We thank the reviewers for their valuable comments. This work is supported in parts by NSFC (61802265, 41671387, 61761146002, 61861130365), LHTD (20170003), Guangdong Provincial Natural Science Foundation (2018A030310426, 2015A030312015), and the National Engineering Laboratory for Big Data System Computing Technology.
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- Gestalt laws
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design