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    This study introduces a new hierarchical fuzzy-cluster-aware grid layout method to analyze ambiguous data points in large datasets. The method enhances visualization for better fuzzy cluster analysis and model diagnosis.

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    Area of Science:

    • Data Visualization
    • Machine Learning
    • Cluster Analysis

    Background:

    • Fuzzy clusters, with ambiguous samples belonging to multiple groups, are prevalent in real-world data.
    • Analyzing these ambiguous samples in large datasets is vital for applications like machine learning model diagnostics.
    • Existing hierarchical cluster-aware grid visualizations struggle to clarify ambiguity within fuzzy clusters.

    Purpose of the Study:

    • To develop a hierarchical fuzzy-cluster-aware grid layout method for improved analysis of large-scale datasets with fuzzy clusters.
    • To enhance the clarity of fuzzy cluster analysis and visual continuity during hierarchical exploration.
    • To address the limitations of current methods in clarifying ambiguity in fuzzy cluster analysis.

    Main Methods:

    • Proposes a novel hierarchical fuzzy-cluster-aware grid layout method.
    • Introduces a two-step optimization strategy for enhanced cluster perception, ambiguity clarification, and stability.
    • Step 1: Creates cluster-aware partitions to enhance perception and maintain cluster stability.
    • Step 2: Generates grid layouts within partitions, positioning ambiguous samples at boundaries and preserving sample-level stability.

    Main Results:

    • The proposed method effectively enhances cluster perception and clarifies ambiguity in fuzzy clusters.
    • It maintains visual continuity and stability at both cluster and sample levels during hierarchical exploration.
    • Demonstrates improved effectiveness in analyzing large-scale datasets, particularly for fuzzy cluster analysis.

    Conclusions:

    • The hierarchical fuzzy-cluster-aware grid layout method offers a significant advancement for analyzing large-scale datasets with fuzzy clusters.
    • It provides a robust solution for clarifying data ambiguity and improving machine learning model diagnostics.
    • The method facilitates deeper insights into complex data structures through enhanced visualization techniques.