Unsupervised learning is used with the K-means clustering technique. Because you are not attempting to predict something, unsupervised learning does not employ labels. Instead, you're looking for a mechanism to arrange your data into clusters based on similar traits.
In Supervised learning, we use a test or validation set to verify the generalization properties of the model in order to avoid over-fitting. However, because you don't know the real clusters of the data in unsupervised learning, you can't evaluate this. As a result, there's no purpose in using a test set.