  PPGMMGA is a Projection Pursuit (PP) algorithm aimed at detecting and
visualizing clustering structures in multivariate data. The algorithm uses the
negentropy as PP index obtained by fitting Gaussian Mixture Models (GMMs) for
density estimation, and then optimized using Genetic Algorithms (GAs). Since
the PPGMMGA algorithm is a dimension reduction technique specifically
introduced for visualization purposes, cluster memberships are not explicitly
provided. In this paper a modal clustering approach is proposed for estimating
clusters of projected data points. In particular, a modal EM algorithm is
employed to estimate the modes corresponding to the local maxima in the
projection subspace of the underlying density estimated using parsimonious
GMMs. Data points are then clustered according to the domain of attraction of
the identified modes. Simulated and real data are discussed to illustrate the
proposed method and evaluate the clustering performance.
