# What is the borderline between unsupervised learning and regular algorithms?

Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets.

However, some algorithms, k-means clustering, for example, are considered unsupervised learning, while they look just regular algorithms (non-ML).

What should be the borderline (criteria) to differentiate between unsupervised learning and a non-ML algorithm?

• data here are possibly statistical data to be specific, because some optimisation algorithms in graph theory are also optimising for better values after a number of iterations on the graph data Aug 24 '21 at 12:53