Combining clustering with dimensionality reduction enhances insights in high-dimensional datasets. Techniques like PCA and KMeans or DBSCAN can simplify data and reveal patterns. Silhouette scores evaluate cluster quality, guiding method selection. Effective integration in a structured pipeline promotes efficiency and reproducibility in analysis.