What are the limitations of K-Means in real-world datasets?
Updated May 16, 2026
Short answer
K-Means struggles with non-spherical clusters, varying densities, and categorical data.
Deep explanation
K-Means assumes isotropic variance and equal cluster density. It also cannot naturally handle categorical or mixed-type data.
Real-world example
Image segmentation where object shapes are irregular.
Common mistakes
- Using K-Means as a universal clustering method.
Follow-up questions
- Which algorithms handle non-spherical clusters?
- Why does density matter?