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    GlossaryPrincipal Component Analysis

    statistical method reducing data dimensions while preserving variance

    Principal Component Analysis (PCA) is a statistical technique used to simplify complex datasets by transforming them into a set of new, uncorrelated variables called principal components. These components capture the most significant patterns in the data, making it easier to visualize and analyze, especially in fields like genetics and biology where large datasets are common.

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