  In the modal approach to clustering, clusters are defined as the local maxima
of the underlying probability density function, where the latter can be
estimated either non-parametrically or using finite mixture models. Thus,
clusters are closely related to certain regions around the density modes, and
every cluster corresponds to a bump of the density. The Modal EM algorithm is
an iterative procedure that can identify the local maxima of any density
function. In this contribution, we propose a fast and efficient Modal EM
algorithm to be used when the density function is estimated through a finite
mixture of Gaussian distributions with parsimonious component-covariance
structures. After describing the procedure, we apply the proposed Modal EM
algorithm on both simulated and real data examples, showing its high
flexibility in several contexts.
