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Updated: Jan 10, 2026

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Visual Extraction of Interaction Patterns Guided by Hierarchical Clustering and Process Mining.

Peilin Yu, Aida Nordman, Takanori Fujiwara

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    This summary is machine-generated.

    This study presents a visual analytics approach for analyzing large user interaction data. It helps uncover patterns in unstructured interaction sequences using clustering and process mining for improved system usability.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Data Mining

    Background:

    • Analyzing user behaviors and improving system usability relies on understanding user interactions.
    • Large, unstructured interaction sequence data presents challenges in pattern discovery.

    Purpose of the Study:

    • To introduce a visual analytics approach for exploring large, unstructured interaction sequence data.
    • To support analysts in uncovering meaningful interaction patterns.

    Main Methods:

    • Integrated hierarchical clustering and process mining techniques.
    • Employed a dynamic time warping-based similarity measure for sequence comparison.
    • Provided stepwise, interactive navigation of clustering results with visual cues.

    Main Results:

    • Successfully enabled analysts to explore large interaction sequence data.
    • Facilitated the progressive uncovering of meaningful interaction patterns.
    • Demonstrated effectiveness and applicability through case studies.

    Conclusions:

    • The visual analytics approach effectively supports the analysis of user interaction data.
    • Integration of clustering and process mining aids in pattern discovery and system usability improvements.
    • The system is applicable for system designers, developers, and domain experts.