  Nonnegative Matrix Factorization (NMF) was first introduced as a low-rank
matrix approximation technique, and has enjoyed a wide area of applications.
Although NMF does not seem related to the clustering problem at first, it was
shown that they are closely linked. In this report, we provide a gentle
introduction to clustering and NMF before reviewing the theoretical
relationship between them. We then explore several NMF variants, namely Sparse
NMF, Projective NMF, Nonnegative Spectral Clustering and Cluster-NMF, along
with their clustering interpretations.
