Quality metrics in high-dimensional data visualization

An overview and systematization

Enrico Bertini, Andrada Tatu, Daniel Keim

    Research output: Contribution to journalArticle

    Abstract

    In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.

    Original languageEnglish (US)
    Article number6064985
    Pages (from-to)2203-2212
    Number of pages10
    JournalIEEE Transactions on Visualization and Computer Graphics
    Volume17
    Issue number12
    DOIs
    StatePublished - 2011

    Fingerprint

    Data visualization
    Visualization
    Pipelines

    Keywords

    • High-Dimensional Data Visualization
    • Quality Metrics

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

    Cite this

    Quality metrics in high-dimensional data visualization : An overview and systematization. / Bertini, Enrico; Tatu, Andrada; Keim, Daniel.

    In: IEEE Transactions on Visualization and Computer Graphics, Vol. 17, No. 12, 6064985, 2011, p. 2203-2212.

    Research output: Contribution to journalArticle

    Bertini, Enrico ; Tatu, Andrada ; Keim, Daniel. / Quality metrics in high-dimensional data visualization : An overview and systematization. In: IEEE Transactions on Visualization and Computer Graphics. 2011 ; Vol. 17, No. 12. pp. 2203-2212.
    @article{69e8baa523e74068bb1a89648106dfc6,
    title = "Quality metrics in high-dimensional data visualization: An overview and systematization",
    abstract = "In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.",
    keywords = "High-Dimensional Data Visualization, Quality Metrics",
    author = "Enrico Bertini and Andrada Tatu and Daniel Keim",
    year = "2011",
    doi = "10.1109/TVCG.2011.229",
    language = "English (US)",
    volume = "17",
    pages = "2203--2212",
    journal = "IEEE Transactions on Visualization and Computer Graphics",
    issn = "1077-2626",
    publisher = "IEEE Computer Society",
    number = "12",

    }

    TY - JOUR

    T1 - Quality metrics in high-dimensional data visualization

    T2 - An overview and systematization

    AU - Bertini, Enrico

    AU - Tatu, Andrada

    AU - Keim, Daniel

    PY - 2011

    Y1 - 2011

    N2 - In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.

    AB - In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.

    KW - High-Dimensional Data Visualization

    KW - Quality Metrics

    UR - http://www.scopus.com/inward/record.url?scp=80855141835&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=80855141835&partnerID=8YFLogxK

    U2 - 10.1109/TVCG.2011.229

    DO - 10.1109/TVCG.2011.229

    M3 - Article

    VL - 17

    SP - 2203

    EP - 2212

    JO - IEEE Transactions on Visualization and Computer Graphics

    JF - IEEE Transactions on Visualization and Computer Graphics

    SN - 1077-2626

    IS - 12

    M1 - 6064985

    ER -