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InterAxis: Steering Scatterplot Axes via Observation-Level Interaction.

Hannah Kim, Jaegul Choo, Haesun Park

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    Summary
    This summary is machine-generated.

    InterAxis enhances scatterplot usability by allowing users to directly define and modify axes. This visual analytics technique improves interpretability and interactivity for multidimensional data exploration.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Visual Analytics

    Background:

    • Scatterplots visualize multidimensional data using Cartesian coordinates, with axes typically bound to single attributes.
    • Interactive exploration involves changing axis-bound attributes, but this can be challenging for complex data, especially with dimension reduction outputs.
    • Current scatterplot axes lack intuitive interpretability and interactivity for users, hindering effective data exploration.

    Purpose of the Study:

    • To introduce InterAxis, a novel visual analytics technique for scatterplots.
    • To enhance the interpretability and interactivity of scatterplot axes for user-driven data exploration.
    • To enable users to define, modify, and tune axes in a more intuitive and direct manner.

    Main Methods:

    • InterAxis allows users to define and modify axes by directly manipulating data items on the x and y axes.
    • The system computes linear combinations of data attributes based on user input to bind them to axes.
    • Users can tune the contribution of data attributes to complex axes through direct visual feedback.

    Main Results:

    • The InterAxis technique provides a user-driven approach to axis definition and modification in scatterplots.
    • It addresses usability challenges related to interpretability and interactivity of scatterplot axes.
    • Demonstrated through two scenarios, showcasing its practical application in data exploration.

    Conclusions:

    • InterAxis offers a significant improvement in scatterplot usability for exploring multidimensional data.
    • The technique empowers users to interactively shape and understand complex data representations.
    • This approach facilitates more effective and intuitive visual analytics, particularly for high-dimensional datasets.