Abstract
Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.
| Original language | English (US) |
|---|---|
| Article number | 6875982 |
| Pages (from-to) | 1683-1692 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Volume | 20 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 31 2014 |
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Keywords
- overdraw reduction
- sampling
- Scatterplot
- visual abstraction
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design
Cite this
Visual abstraction and exploration of multi-class scatterplots. / Chen, Haidong; Chen, Wei; Mei, Honghui; Liu, Zhiqi; Zhou, Kun; Chen, Weifeng; Gu, Wentao; Ma, Kwan-Liu.
In: IEEE Transactions on Visualization and Computer Graphics, Vol. 20, No. 12, 6875982, 31.12.2014, p. 1683-1692.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Visual abstraction and exploration of multi-class scatterplots
AU - Chen, Haidong
AU - Chen, Wei
AU - Mei, Honghui
AU - Liu, Zhiqi
AU - Zhou, Kun
AU - Chen, Weifeng
AU - Gu, Wentao
AU - Ma, Kwan-Liu
PY - 2014/12/31
Y1 - 2014/12/31
N2 - Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.
AB - Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.
KW - overdraw reduction
KW - sampling
KW - Scatterplot
KW - visual abstraction
UR - http://www.scopus.com/inward/record.url?scp=84910038336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84910038336&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2014.2346594
DO - 10.1109/TVCG.2014.2346594
M3 - Article
C2 - 26356882
AN - SCOPUS:84910038336
VL - 20
SP - 1683
EP - 1692
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 12
M1 - 6875982
ER -