What is the ultimate limitation of K-Means as a clustering paradigm?

Its fundamental limitation is reliance on Euclidean distance and centroid-based representation.

K-Means reduces complex data distributions into point means, which cannot capture multimodality, non-convex shapes, or probabilistic uncertainty. It assumes geometry is simple and linear, which rarely holds in modern high-dimensional or structured data.