midClustering
What is dimensionality reduction in clustering?
Updated May 15, 2026
Short answer
Dimensionality reduction simplifies data while preserving structure for clustering.
Deep explanation
Techniques like PCA reduce feature space, improving clustering performance and visualization.
Real-world example
Visualizing customer clusters in 2D space.
Common mistakes
- Removing too much variance during reduction.
Follow-up questions
- Does PCA always improve clustering?