Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots

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

doi:10.1109/tvcg.2019.2934811

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 journal › Article › peer-review

Abstract

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 language	English
Article number	8848845
Pages (from-to)	770-779
Number of pages	10
Journal	IEEE Transactions on Visualization and Computer Graphics
Volume	26
Issue number	1
DOIs	https://doi.org/10.1109/tvcg.2019.2934811
State	Published - Jan 2020
Externally published	Yes

Bibliographical note

Funding 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.

Publisher Copyright:
© 1995-2012 IEEE.

Keywords

Association
Gestalt laws
Scatterplot
Uncertainty

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/tvcg.2019.2934811

Cite this

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title = "Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots",

abstract = "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.",

keywords = "Association, Gestalt laws, Scatterplot, Uncertainty",

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

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doi = "10.1109/tvcg.2019.2934811",

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T2 - Visualizing Association with Uncertainty in Multi-class Scatterplots

AU - Lu, Min

AU - Wang, Shuaiqi

AU - Lanir, Joel

AU - Fish, Noa

AU - Yue, Yang

AU - Cohen-Or, Daniel

AU - Huang, Hui

N1 - Funding 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. Publisher Copyright: © 1995-2012 IEEE.

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N2 - 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.

AB - 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.

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Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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