Principal component analysis

Principal component analysis (PCA) transforms the attributes of unlabeled data using a simple rearrangement and transformation with rotation. Looking at the data that does not have any significance, you can find ways to reduce dimensions this way. For instance, when a particular dataset looks similar to an ellipse when run at a particular angle to the axes, while in another transformed representation moves along the x axis and clearly has signs of no variation along the y axis, then it may be possible to ignore that.

k-means clustering is appropriate to cluster unlabeled data. Sometimes, one can use PCA to project data to a much lower dimension and then apply other methods, such as k-means, to a smaller and reduced ...

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