  Non-orthogonal multiple access (NOMA) and mmWave are two complementary
technologies that can support the capacity demand that arises in 5G and beyond
networks. The increasing number of users are served simultaneously while
providing a solution for the scarcity of the bandwidth. In this paper we
present a method for clustering the users in a mmWave-NOMA system with the
objective of maximizing the sum-rate. An unsupervised machine learning
technique, namely, hierarchical clustering is utilized which does the automatic
identification of the optimal number of clusters. The simulations prove that
the proposed method can maximize the sum-rate of the system while satisfying
the minimum QoS for all users without the need of the number of clusters as a
prerequisite when compared to other clustering methods such as k-means
clustering.
