How does feature correlation impact K-Means performance?

Highly correlated features distort distance calculations and bias cluster formation.

When features are correlated, Euclidean distance effectively double-counts similar information, overweighting certain directions in feature space. This leads to elongated or skewed clusters that do not reflect true structure.