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Teradata Aster: Principal Component Analysis and Unsupervised Machine Learning

Please watch my video on Aster's principal component analysis or PCA. I not only show how Aster performs this analytic but I attempt to explain how PCA works and explain eigenvectors and eigenvalues. Genre: Statistical Analysis (Unsupervised Learning) Background: A process used to emphasize variability and bring out strong patterns in a dataset. This variability is expressed by principal components; which are directions of highest degree of variance. The first several principal components represent 80-90% of the variance and hence most important.

Use Cases:

- Dimensional Reduction / Compression / Image Recognition

- Medical Diagnosis / Medical Imaging / Sensor Data

- Outlier Detection

Views: 837

Tags: PCA, analysis, aster, component, eigenvalues, eigenvectors, principal, teradata

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