How does K-Means interact with PCA or dimensionality reduction?

PCA can improve K-Means by reducing noise and concentrating variance into fewer dimensions.

PCA projects data into orthogonal components capturing maximum variance. This reduces noise and improves distance reliability for K-Means, especially in high-dimensional datasets.