Please wait a minute...
Vis Inf
Vis Inf  2019, Vol. 3 Issue (1): 48-57    DOI: 10.1016/j.visinf.2019.03.006
论文     
一种基于关联规则的减少平行集视觉混乱的方法
Chong Zhanga, Yang Chenb, Jing Yanga, Zhengcong Yinc
aUNC Charlotte, United States bI4 Data, United States cTexas A&M University, United States
An association rule based approach to reducing visual clutter in parallel sets
Chong Zhanga, Yang Chenb, Jing Yanga, Zhengcong Yinc
aUNC Charlotte, United States bI4 Data, United States cTexas A&M University, United States
 全文: PDF 
摘要： 平行集是一种流行的类别数据可视化技术，它直观地揭示了基于频率的关系细节，但对高维类别数据集，该方法会带来混乱的视觉显示，严重影响对类别数据间关系的探索。关联规则挖掘是发现类别变量之间关系的流行方法。它是平行集方法的一种补充，以有意义的方式对带状关联进行分组。尽管如此，仍然难以理解从高维类别数据集中发现的大量规则。在本文中，我们将这两种方法整合到一个可视化分析系统中，通过二分结果来探索高维类别数据。该系统不仅有助于用户直观地理解关联规则，还能提供一种有效的降维和减少类别的方法，实现更为清晰、更有组织的可视化。我们的方法的有效性和效率通过一组用户研究和对基准数据集的实验得以验证。
关键词： 关联规则;  平行集;  视觉混乱;  可视化分析    
Abstract: Although Parallel Sets, a popular categorical data visualization technique, intuitively reveals the frequency based relationships in details, a high-dimensional categorical dataset brings a cluttered visual display that seriously obscures the relationship explorations. Association rule mining is a popular approach to discovering relationships among categorical variables. It could complement Parallel Sets to group ribbons in a meaningful way. However, it is difficult to understand a larger number of rules discovered from a high-dimensional categorical dataset. In this paper, we integrate the two approaches into a visual analytics system for exploring high-dimensional categorical data with dichotomous outcome. The system not only helps users interpret association rules intuitively, but also provides an effective dimension and category reduction approach towards a less clustered and more organized visualization. The effectiveness and efficiency of our approach are illustrated by a set of user studies and experiments with benchmark datasets.
Key words: Association rule    Parallel sets    Visual clutter    Visual analytics
出版日期: 2019-04-01
通讯作者: Chong Zhang     E-mail: ChongZhang.NC@gmail.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Chong Zhang
Yang Chen
Jing Yang
Zhengcong Yin

引用本文:

Chong Zhang, Yang Chen, Jing Yang, Zhengcong Yin. An association rule based approach to reducing visual clutter in parallel sets. Vis Inf, 2019, 3(1): 48-57.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2019.03.006    或    http://www.zjujournals.com/vi/CN/Y2019/V3/I1/48

[1] Takanori Fujiwara, Tarik Crnovrsanin, Kwan-Liu Ma. 交互式网络分析过程的简明概括  [J]. Vis Inf, 2018, 2(4): 213-224.
[2] Christopher Collins, Natalia Andrienko, TobiasSchreck, JingYang, Jaegul Choo, Ulrich Engelke, Amit Jena, Tim Dwyer. 人机分析过程导引 [J]. Vis Inf, 2018, 2(3): 166-180.
[3] Yusuke Ishikawa, Issei fujishiro. TideGrapher：橄榄球比赛战术的可视分析[J]. Vis Inf, 2018, 2(1): 60-70.