How does feature space transformation improve data mining performance?

It transforms raw data into more separable and informative representations.

Feature space transformation includes techniques like kernel mapping, PCA, and nonlinear embeddings that convert raw input into spaces where patterns become easier to detect. This improves clustering, classification, and anomaly detection by increasing separability and reducing noise.