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    Area of Science:

    • Data Science
    • Computer Science
    • Information Visualization

    Background:

    • Dimension reduction and clustering algorithms are key visual analytics tools for analyzing data similarity and group structures.
    • Historically, these algorithms were used independently, but recent trends integrate them into single visualization systems.
    • Current integrated approaches are often disconnected, lacking interdependence between dimension reduction and clustering.

    Purpose of the Study:

    • To provide an overview of combining dimension reduction and clustering algorithms within visualization systems.
    • To identify and discuss the inherent challenges in developing integrated visualization systems that utilize both algorithm families.
    • To inform the design of more cohesive and interdependent algorithmic combinations.

    Main Methods:

    • Literature review of existing visual analytics systems incorporating dimension reduction and clustering.
    • Analysis of design decisions in concurrent algorithm application: algorithm selection, processing order, and interaction design.
    • Identification of challenges in creating interdependent algorithmic frameworks.

    Main Results:

    • Existing systems often combine dimension reduction and clustering in an ad hoc or parallel manner.
    • Significant design decisions are required for effective integration, including algorithm choice and processing sequence.
    • A lack of interdependence between the two algorithm families is a common issue.

    Conclusions:

    • Combining dimension reduction and clustering offers potential for enhanced data analysis but presents significant design challenges.
    • Developing integrated systems requires careful consideration of algorithmic interdependence and user interaction.
    • Further research is needed to create more cohesive and effective frameworks for combining these techniques.