Visual Clustering Factors in Scatterplots

Jiazhi Xia, Weixing Lin, Guang Jiang, Yunhai Wang, Wei Chen, Tobias Schreck

Research output: Contribution to journalArticlepeer-review


Cluster analysis is an important technique in data analysis. However, there is no encompassing theory on scatterplots to evaluate clustering. Human visual perception is regarded as a gold standard to evaluate clustering. The cluster analysis based on human visual perception requires the participation of many probands, to obtain diverse data, and hence is a challenge to do. We contribute an empirical and data-driven study on human perception for visual clustering of large scatterplot data. First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual factors on clustering perception. Third, we use the labeled datasets to train a deep neural network for modeling human visual clustering perception. Our experiments show that the data-driven model successfully models the human visual perception, and outperforms conventional clustering algorithms in synthetic and real datasets.

Original languageEnglish
Article number9495208
Pages (from-to)79-89
Number of pages11
JournalIEEE Computer Graphics and Applications
Issue number5
Publication statusPublished - 1 Sep 2021

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing


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