  By combining related objects, unsupervised machine learning techniques aim to
reveal the underlying patterns in a data set. Non-negative Matrix Factorization
(NMF) is a data mining technique that splits data matrices by imposing
restrictions on the elements' non-negativity into two matrices: one
representing the data partitions and the other to represent the cluster
prototypes of the data set. This method has attracted a lot of attention and is
used in a wide range of applications, including text mining, clustering,
language modeling, music transcription, and neuroscience (gene separation). The
interpretation of the generated matrices is made simpler by the absence of
negative values. In this article, we propose a study on multi-modal clustering
algorithms and present a novel method called multi-modal multi-view
non-negative matrix factorization, in which we analyze the collaboration of
several local NMF models. The experimental results show the value of the
proposed approach, which was evaluated using a variety of data sets, and the
obtained results are very promising compared to state of art methods.
