midClustering
What is the curse of dimensionality in clustering?
Updated May 15, 2026
Short answer
As dimensions increase, distance metrics become less meaningful.
Deep explanation
High-dimensional data makes points appear equally distant, reducing clustering effectiveness.
Real-world example
Text clustering with thousands of features.
Common mistakes
- Ignoring dimensionality issues in high-feature datasets.
Follow-up questions
- How to solve curse of dimensionality?