Why PCA Improves Clustering Stability

Principal Component Analysis reduces dimensionality while preserving variance.

Why It Helps K-Means

• Removes noise
• Reduces correlated features
• Improves cluster separation

In high-dimensional space, distance metrics become unstable. PCA improves signal-to-noise ratio.