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To visualize multi-dimensional trends and clusters in Excel data, you can use a workflow that loads structured spreadsheets, cleans missing values, applies KMeans clustering, and generates trend, distribution, and sensitivity charts.

Identifying group structures in large spreadsheet datasets is best achieved through clustering analysis, which standardizes features, handles missing values, and applies KMeans to output cluster labels and distribution visualizations.

Yes, you can use pandas and scikit-learn to clean Excel files and generate trend charts by loading structured data, performing feature engineering, applying clustering, and visualizing results with seaborn or matplotlib.

This data analysis approach supports large Excel files for cross-sectional comparisons by converting spreadsheets to Parquet for efficient processing, dynamically identifying headers, and generating multi-dimensional visualizations.

To perform parameter sensitivity analysis on structured spreadsheet data, the workflow executes feature engineering and clustering, then outputs visualizations that highlight distribution characteristics and parameter sensitivities across clusters.