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XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data.

Sehi L'Yi, Bongkyung Ko, DongHwa Shin

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    Assessing bioinformatics clustering results is difficult. XCluSim is a visual analytics tool that interactively compares multiple clustering results, simplifying this process for researchers.

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    Area of Science:

    • Bioinformatics
    • Data Visualization
    • Computational Biology

    Background:

    • Cluster analysis is essential in bioinformatics but assessing result quality is challenging.
    • Comparing multiple clustering algorithms and parameters is labor-intensive and cognitively demanding.
    • Existing visualization techniques for clustering results lack a systematic categorization.

    Purpose of the Study:

    • To introduce XCluSim, a visual analytics tool for interactive comparison of multiple clustering results.
    • To develop a taxonomy for clustering result visualization techniques based on Gestalt principles.
    • To enable researchers to efficiently select optimal clustering methods and parameters.

    Main Methods:

    • Developed a taxonomy of clustering result visualization techniques using Gestalt principles.
    • Integrated interactive visualizations tailored to different clustering algorithm outputs.
    • Designed XCluSim based on the Visual Information Seeking Mantra for intuitive user interaction.

    Main Results:

    • XCluSim provides scalable comparison of multiple clustering results.
    • The tool supports diverse clustering algorithms and offers dedicated visualizations.
    • Case studies demonstrated XCluSim's efficacy in identifying stable clusters.

    Conclusions:

    • XCluSim enhances the scalability and effectiveness of comparing clustering results.
    • The tool facilitates detailed exploration on demand through tailored visualizations and interactions.
    • Positive feedback from bioinformaticians highlights XCluSim's utility in identifying stable clusters across analyses.