Quality metrics for 2D scatterplot graphics: Automatically reducing visual clutter

Enrico Bertini, Giuseppe Santucci

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    The problem of visualizing huge amounts of data is very well known in the field of Computer Graphics. Visualizing large number of items (the order of millions) forces almost any kind of techniques to reveal its limits in terms of expressivity and scalability. To deal with this problem we propose a "feature preservation" approach, based on the idea of modelling the final visualization in a virtual space in order to analyze its features (e.g, absolute and relative density, clusters, etc.). Through this approach we provide a formal model to measure the visual clutter resulting from the representation of a large dataset on a physical device, obtaining some figures about the visualization decay and devising an automatic sampling strategy able to preserve relative densities.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsAndreas Butz, Antonio Kruger, Patrick Olivier
    PublisherSpringer Verlag
    Pages77-89
    Number of pages13
    ISBN (Electronic)9783540219774
    DOIs
    StatePublished - 2004

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3031
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Bertini, E., & Santucci, G. (2004). Quality metrics for 2D scatterplot graphics: Automatically reducing visual clutter. In A. Butz, A. Kruger, & P. Olivier (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 77-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3031). Springer Verlag. https://doi.org/10.1007/978-3-540-24678-7_8