  Mixture models are among the most popular tools for clustering. However, when
the dimension and the number of clusters is large, the estimation of the
clusters become challenging, as well as their interpretation. Restriction on
the parameters can be used to reduce the dimension. An example is given by
mixture of factor analyzers for Gaussian mixtures. The extension of MFA to
non-Gaussian mixtures is not straightforward. We propose a new constraint for
parameters in non-Gaussian mixture model: the $K$ components parameters are
combinations of elements from a small dictionary, say $H$ elements, with $H \ll
K$. Including a nonnegative matrix factorization (NMF) in the EM algorithm
allows us to simultaneously estimate the dictionary and the parameters of the
mixture. We propose the acronym NMF-EM for this algorithm, implemented in the R
package {\tt nmfem}. This original approach is motivated by passengers
clustering from ticketing data: we apply NMF-EM to data from two Transdev
public transport networks. In this case, the words are easily interpreted as
typical slots in a timetable.
