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, the purpose of test (and frequently validation) sets is to verify the generalization properties of your model in order to avoid over-fitting. However, since you don't know the real clusters of the data in unsupervised learning, you can't evaluate this. As a result, employing a test set is pointless.