• Login
    View Item 
    •   Eurographics DL Home
    • Eurographics Partner Events
    • EuroRVVV: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization
    • TrustVis19
    • View Item
    •   Eurographics DL Home
    • Eurographics Partner Events
    • EuroRVVV: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization
    • TrustVis19
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Towards Supporting Interpretability of Clustering Results with Uncertainty Visualization

    Thumbnail
    View/Open
    001-005.pdf (1.163Mb)
    Date
    2019
    Author
    Kinkeldey, Christoph
    Korjakow, Tim
    Benjamin, Jesse Josua
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    Interpretation of machine learning results is a major challenge for non-technical experts, with visualization being a common approach to support this process. For instance, interpretation of clustering results is usually based on scatterplots that provide information about cluster characteristics implicitly through the relative location of objects. However, the locations and distances tend to be distorted because of artifacts stemming from dimensionality reduction. This makes interpretation of clusters difficult and may lead to distrust in the system. Most existing approaches that counter this drawback explain the distances in the scatterplot (e.g., error visualization) to foster the interpretability of implicit information. Instead, we suggest explicit visualization of the uncertainty related to the information needed for interpretation, specifically the uncertain membership of each object to its cluster. In our approach, we place objects on a grid, and add a continuous ''topography'' in the background, expressing the distribution of uncertainty over all clusters. We motivate our approach from a use case in which we visualize research projects, clustered by topics extracted from scientific abstracts. We hypothesize that uncertainty visualization can increase trust in the system, which we specify as an emergent property of interaction with an interpretable system. We present a first prototype and outline possible procedures for evaluating if and how the uncertainty visualization approach affects interpretability and trust.
    BibTeX
    @inproceedings {10.2312:trvis.20191183,
    booktitle = {EuroVis Workshop on Trustworthy Visualization (TrustVis)},
    editor = {Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska},
    title = {{Towards Supporting Interpretability of Clustering Results with Uncertainty Visualization}},
    author = {Kinkeldey, Christoph and Korjakow, Tim and Benjamin, Jesse Josua},
    year = {2019},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-091-8},
    DOI = {10.2312/trvis.20191183}
    }
    URI
    https://doi.org/10.2312/trvis.20191183
    https://diglib.eg.org:443/handle/10.2312/trvis20191183
    Collections
    • TrustVis19

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA