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20.5 Principal Component Analysis
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Principal Component Analysis (PCA) is the younger brother of ICM's more powerful data analysis tools, like property prediction and clustering, though it still may give a good description of the data with a few columns or even chemical compounds. PCA is a mathematical procedure that transforms a number of correlated variables into a number of smaller uncorrelated variables known as Principal Components The first component accounts for as much of the variability as possible with the rest of the components accounting for the remainder. PCA may be very helpful when you believe the data actually contains only a few meaningful components. Principal components are linear combinations of the provided data columns.

To perform a PCA analysis a table (either chemical or standard ICM table) needs to loaded into ICM. For information regarding ICM Tables and ICM Chemical Tables please follow these links.

To begin the PCA procedure


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PLS and Principal Component regression and classification