How does PCA affect clustering stability across different runs?

PCA improves clustering stability by reducing noise and dimensionality variability.

Clustering algorithms like K-Means are sensitive to noise and high-dimensional sparsity. PCA reduces these effects by projecting data into a stable subspace where distance relationships are more meaningful. This leads to more consistent cluster assignments across runs.