We investigate statistical properties of Cluster-Weighted Modeling, which is
a framework for supervised learning originally developed in order to recreate a
digital violin with traditional inputs and realistic sound. The analysis is
carried out in comparison with Finite Mixtures of Regression models. Based on
some geometrical arguments, we highlight that Cluster-WeightedModeling provides
a quite general framework for local statistical modeling. Theoretical results
are illustrated on the ground of some numerical simulations.