Towards understanding human similarity perception in the analysis of large sets of scatter plots

Anshul Vikram Pandey, Josua Krause, Cristian Felix, Jeremy Boy, Enrico Bertini

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present a study aimed at understanding how human observers judge scatter plot similarity when presented with a large set of iconic scatter plot representations. The work we present involves 18 participants with a scientific background in a similarity perception study. The study asks participants to group a carefully selected set of plots according to their subjective perceptual judgement of similarity, and it integrates the results into a consensus similarity grouping. We then use this consensus grouping to generate insights on similarity perception. The main output of this work is a list of concepts we derive to describe major perceptual features, and a description of how these concepts relate and rank. We also evaluate scagnostics (scatter plot diagnostics), a popular and established set of scatter plot descriptors, and show that they do not reliably reproduce our participants judgements. Finally, we discuss the major implications of this study and how these results can be used for future research.

    Original languageEnglish (US)
    Title of host publicationCHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
    PublisherAssociation for Computing Machinery
    Pages3659-3669
    Number of pages11
    ISBN (Electronic)9781450333627
    DOIs
    StatePublished - May 7 2016
    Event34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose, United States
    Duration: May 7 2016May 12 2016

    Other

    Other34th Annual Conference on Human Factors in Computing Systems, CHI 2016
    CountryUnited States
    CitySan Jose
    Period5/7/165/12/16

    Keywords

    • Human perception modeling
    • Information visualization
    • Plot similarity
    • Quality measures

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Graphics and Computer-Aided Design
    • Software

    Cite this

    Pandey, A. V., Krause, J., Felix, C., Boy, J., & Bertini, E. (2016). Towards understanding human similarity perception in the analysis of large sets of scatter plots. In CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems (pp. 3659-3669). Association for Computing Machinery. https://doi.org/10.1145/2858036.2858155

    Towards understanding human similarity perception in the analysis of large sets of scatter plots. / Pandey, Anshul Vikram; Krause, Josua; Felix, Cristian; Boy, Jeremy; Bertini, Enrico.

    CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2016. p. 3659-3669.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Pandey, AV, Krause, J, Felix, C, Boy, J & Bertini, E 2016, Towards understanding human similarity perception in the analysis of large sets of scatter plots. in CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, pp. 3659-3669, 34th Annual Conference on Human Factors in Computing Systems, CHI 2016, San Jose, United States, 5/7/16. https://doi.org/10.1145/2858036.2858155
    Pandey AV, Krause J, Felix C, Boy J, Bertini E. Towards understanding human similarity perception in the analysis of large sets of scatter plots. In CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2016. p. 3659-3669 https://doi.org/10.1145/2858036.2858155
    Pandey, Anshul Vikram ; Krause, Josua ; Felix, Cristian ; Boy, Jeremy ; Bertini, Enrico. / Towards understanding human similarity perception in the analysis of large sets of scatter plots. CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2016. pp. 3659-3669
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