Visual Clustering Factors in Scatterplots

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

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

Abstract

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.

Originalspracheenglisch
Aufsatznummer9495208
Seiten (von - bis)79-89
Seitenumfang11
FachzeitschriftIEEE Computer Graphics and Applications
Jahrgang41
Ausgabenummer5
DOIs
PublikationsstatusVeröffentlicht - 1 Sep. 2021

ASJC Scopus subject areas

  • Software
  • Computergrafik und computergestütztes Design

Fields of Expertise

  • Information, Communication & Computing

Fingerprint

Untersuchen Sie die Forschungsthemen von „Visual Clustering Factors in Scatterplots“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren