Journal of Visualization

, Volume 22, Issue 5, pp 913–926 | Cite as

FuzzyRadar: visualization for understanding fuzzy clusters

  • Fangfang Zhou
  • Bing Bai
  • Yitao Wu
  • Minghui Chen
  • Zengsheng Zhong
  • Rongchen Zhu
  • Yi Chen
  • Ying ZhaoEmail author
Regular Paper


Fuzzy clustering assigns a membership degree (MD) on a datum to a cluster, which reflects real-world clustering scenarios but increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that multidimensional visualization techniques are beneficial to fuzzy clusters analysis. However, empirically, no single existing visualization technique can support most analytical tasks featured by fuzzy clustering. This work proposes a new visualization called FuzzyRadar for understanding fuzzy clusters. Its basic idea is to combine the advantages of radial coordinate visualization (Radviz), which specializes in data-oriented analytical tasks, and parallel coordinate plot (PCP), which performs well in cluster-oriented analytical tasks. First, we adopt a compact and compounded layout to integrate Radviz and PCP into one visualization view. Then, we introduce a strip-edge-bundling method to reduce the visual cluster caused by PCP polylines and a histogram embedding method to facilitate the recognition of MD distribution. We also provide a group of additional visual encodings and a set of lightweight interactions. Finally, we use a case study to demonstrate the usability of FuzzyRadar and conduct a controlled quantitative evaluation to compare the performance of FuzzyRadar, Radviz, PCP, and scatterplot matrix. Result shows that FuzzyRadar supports all the seven examined analytical tasks well and presents a significant capability improvement compared with Radviz and PCP.

Graphic abstract


Visualization Visual analysis Fuzzy clustering Radviz Parallel coordinate plot 



This work is supported by the National Key Research and Development Program of China No. 2018YFB0904503, the National Science and Technology Fundamental Resources Investigation Program of China No. 2018FY10090002, the National Natural Science Foundation of China Nos. 61672538 and 61872388, and the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety (Beijing Technology and Business University) No. BKBD-2018KF08.

Supplementary material

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Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  • Fangfang Zhou
    • 1
  • Bing Bai
    • 2
  • Yitao Wu
    • 1
  • Minghui Chen
    • 1
  • Zengsheng Zhong
    • 1
  • Rongchen Zhu
    • 1
  • Yi Chen
    • 3
  • Ying Zhao
    • 1
    Email author
  1. 1.School of Computer Science and EngineerCentral South UniversityChangshaChina
  2. 2.School of AutomationCentral South UniversityChangshaChina
  3. 3.Beijing Key Laboratory of Big Data Technology for Food SafetyBeijing Technology and Business UniversityBeijingChina

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