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Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data

机译：基于层次聚类的有序节点森伯斯特：关联的农药残留层次数据的可视化分析方法

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摘要

According to the characteristics of pesticide residue data and analyzing requirements in food safety fields, we presented a visual analyzing method for associated hierarchical data, called sunburst with ordered nodes based on hierarchical clustering (SONHC). SONHC arranged the leaf nodes in sunburst in order using hierarchical clustering algorithm, put the associated dataset as a node in center of the sunburst, and connected it with the associated leaf nodes in sunburst using colored lines. So, it can present not only two hierarchical structures but also the relationships between them. Based on SONHC and some interaction techniques (clicking, contraction and expansion, etc) we developed an associated visual analyzing system (AVAS) for pesticide residues detection results data, which can help users to inspect the hierarchical structure of pesticide and agricultural products and to explore the associations between pesticides and agricultural products, and associations between different pesticides. The results of user experience test showed that SONHC algorithm overperforms than SA and SR algorithm in ULE and ULE's variance. AVAS system is effective in helping users to analyze the pesticide residues data. Furthermore, SONHC algorithm can also be adopted to analyze associated hierarchical data in other fields, such as finance, insurance and e-commerce.
机译：根据农药残留数据的特点和食品安全领域的分析要求，提出了一种基于层次聚类（SONHC）的关联层次数据可视化分析方法，即有序节点的森伯斯特。 SONHC使用层次聚类算法按顺序排列了森伯斯特中的叶节点，将关联数据集作为森伯斯特中心的节点，并使用彩色线将其与森伯斯特中的关联叶节点连接。因此，它不仅可以呈现两个层次结构，还可以呈现它们之间的关系。基于SONHC和一些交互技术（单击，收缩和扩展等），我们开发了用于农药残留检测结果数据的关联视觉分析系统（AVAS），可以帮助用户检查农药和农产品的层次结构并探索农药与农产品之间的联系，以及不同农药之间的联系。用户体验测试结果表明，在ULE和ULE的方差方面，SONHC算法的性能优于SA和SR算法。 AVAS系统可有效帮助用户分析农药残留数据。此外，还可以采用SONHC算法来分析金融，保险和电子商务等其他领域的关联层次数据。

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  • 来源
    《Journal of visualization》 |2015年第2期|237-254|共18页
  • 作者

    Yi Chen; Xinyue Zhang; Yuchao Feng; Jie Liang; Hongqian Chen;

  • 作者单位

    School of Computer and Information Engineering, Beijing Technology and Business University (BTBU), Beijing 100048, People's Republic of China;

    School of Computer and Information Engineering, Beijing Technology and Business University (BTBU), Beijing 100048, People's Republic of China;

    School of Computer and Information Engineering, Beijing Technology and Business University (BTBU), Beijing 100048, People's Republic of China;

    Key Laboratory of Machine Perception (Ministry of Education) and School of EECS, Peking University, Beijing, China;

    School of Computer and Information Engineering, Beijing Technology and Business University (BTBU), Beijing 100048, People's Republic of China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Information visualization; Visual analytics; Associated hierarchical data; Sunburst; Pesticide residue;

    机译：信息可视化;视觉分析;关联的分层数据;朝阳农药残留;

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