Towards Supporting Interpretability of Clustering Results with Uncertainty Visualization

dc.contributor.authorKinkeldey, Christophen_US
dc.contributor.authorKorjakow, Timen_US
dc.contributor.authorBenjamin, Jesse Josuaen_US
dc.contributor.editorKosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeskaen_US
dc.date.accessioned2019-06-02T18:16:16Z
dc.date.available2019-06-02T18:16:16Z
dc.date.issued2019
dc.description.abstractInterpretation 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.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationEuroVis Workshop on Trustworthy Visualization (TrustVis)
dc.identifier.doi10.2312/trvis.20191183
dc.identifier.isbn978-3-03868-091-8
dc.identifier.pages1-5
dc.identifier.urihttps://doi.org/10.2312/trvis.20191183
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/trvis20191183
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization design and evaluation methods
dc.subjectInterface design prototyping
dc.subjectHCI theory
dc.subjectconcepts and models
dc.titleTowards Supporting Interpretability of Clustering Results with Uncertainty Visualizationen_US
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