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 data space.

However, it is very important to perform dimension reduction carefully because any dimension reduction may lead to the loss of information, and it is crucial that the algorithm preserves the useful part of the data while discarding the noise...