Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/511630
Title: Visual Abstraction and Exploration of Multi-class Scatterplots
Authors: State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou, China;Haidong Chen ; Wei Chen ; Honghui Mei ; Zhiqi Liu ; Kun Zhou ; Weifeng Chen ; Wentao Gu ; Kwan-Liu Ma
subject: data analysis; data visualisation; sampling methods; clusters; correlations; data analysis; density contrast; feature-preserving simplification; hierarchical multiclass sampling technique; local trends; multiclass point distribution; multiclass scatterplot; outliers; scatter dataset visualization; scatterplot view; visual abstraction; visual abstraction scheme; Data visualization; Estimation; Image color analysis; Market research; Noise; Statistical analysis; Visualization; Scatterplot; overdraw reduction; sampling; visual abstraction;
Year: 2014
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/264383
http://localhost/handle/Hannan/511630
ISSN: 1077-2626
volume: 20
issue: 12
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6875982.pdf2.62 MBAdobe PDF
Title: Visual Abstraction and Exploration of Multi-class Scatterplots
Authors: State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou, China;Haidong Chen ; Wei Chen ; Honghui Mei ; Zhiqi Liu ; Kun Zhou ; Weifeng Chen ; Wentao Gu ; Kwan-Liu Ma
subject: data analysis; data visualisation; sampling methods; clusters; correlations; data analysis; density contrast; feature-preserving simplification; hierarchical multiclass sampling technique; local trends; multiclass point distribution; multiclass scatterplot; outliers; scatter dataset visualization; scatterplot view; visual abstraction; visual abstraction scheme; Data visualization; Estimation; Image color analysis; Market research; Noise; Statistical analysis; Visualization; Scatterplot; overdraw reduction; sampling; visual abstraction;
Year: 2014
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/264383
http://localhost/handle/Hannan/511630
ISSN: 1077-2626
volume: 20
issue: 12
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6875982.pdf2.62 MBAdobe PDF
Title: Visual Abstraction and Exploration of Multi-class Scatterplots
Authors: State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou, China;Haidong Chen ; Wei Chen ; Honghui Mei ; Zhiqi Liu ; Kun Zhou ; Weifeng Chen ; Wentao Gu ; Kwan-Liu Ma
subject: data analysis; data visualisation; sampling methods; clusters; correlations; data analysis; density contrast; feature-preserving simplification; hierarchical multiclass sampling technique; local trends; multiclass point distribution; multiclass scatterplot; outliers; scatter dataset visualization; scatterplot view; visual abstraction; visual abstraction scheme; Data visualization; Estimation; Image color analysis; Market research; Noise; Statistical analysis; Visualization; Scatterplot; overdraw reduction; sampling; visual abstraction;
Year: 2014
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/264383
http://localhost/handle/Hannan/511630
ISSN: 1077-2626
volume: 20
issue: 12
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6875982.pdf2.62 MBAdobe PDF