Visual Clustering Factors in Scatterplots

被引:9
|
作者
Xia, Jiazhi [1 ]
Lin, Weixing [2 ,3 ]
Jiang, Guang [4 ]
Wang, Yunhai [5 ]
Chen, Wei [6 ]
Schreck, Tobias [7 ]
机构
[1] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Hunan, Peoples R China
[2] Cent South Univ, Comp Sci & Technol, Changsha 410083, Hunan, Peoples R China
[3] Cent South Univ, Visual Analyt Lab, Changsha 410083, Hunan, Peoples R China
[4] Cent South Univ, Changsha 410083, Hunan, Peoples R China
[5] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China
[6] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310027, Peoples R China
[7] Graz Univ Technol, Fac Comp Sci & Biomed Engn, Inst Comp Graph & Knowledge Visualizat, A-8070 Graz, Styria, Austria
基金
中国国家自然科学基金;
关键词
Visualization; Shape; Visual perception; Clustering algorithms; Deep learning; Splines (mathematics); Computer science;
D O I
10.1109/MCG.2021.3098804
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Cluster analysis is an important technique in data analysis. However, there is no encompassing theory on scatterplots to evaluate clustering. Human visual perception is regarded as a gold standard to evaluate clustering. The cluster analysis based on human visual perception requires the participation of many probands, to obtain diverse data, and hence is a challenge to do. We contribute an empirical and data-driven study on human perception for visual clustering of large scatterplot data. First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual factors on clustering perception. Third, we use the labeled datasets to train a deep neural network for modeling human visual clustering perception. Our experiments show that the data-driven model successfully models the human visual perception, and outperforms conventional clustering algorithms in synthetic and real datasets.
引用
收藏
页码:79 / 89
页数:11
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