title: Visual Abstraction and Exploration of Multi-class Scatterplots.
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.
authors.forename: Haidong
authors.initials: H
authors.lastname: Chen
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Honghui
authors.initials: H
authors.lastname: Mei
authors.forename: Zhiqi
authors.initials: Z
authors.lastname: Liu
authors.forename: Kun
authors.initials: K
authors.lastname: Zhou
authors.forename: Weifeng
authors.initials: W
authors.lastname: Chen
authors.forename: Wentao
authors.initials: W
authors.lastname: Gu
authors.forename: Kwan-Liu
authors.initials: KL
authors.lastname: Ma
mesh_terms: D000328:Adult; D000465:Algorithms; D003196:Computer Graphics; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D055815:Young Adult