  This paper deals with nonparametric estimation of conditional den-sities in
mixture models in the case when additional covariates are available. The
proposed approach consists of performing a prelim-inary clustering algorithm on
the additional covariates to guess the mixture component of each observation.
Conditional densities of the mixture model are then estimated using kernel
density estimates ap-plied separately to each cluster. We investigate the
expected L 1 -error of the resulting estimates and derive optimal rates of
convergence over classical nonparametric density classes provided the
clustering method is accurate. Performances of clustering algorithms are
measured by the maximal misclassification error. We obtain upper bounds of this
quantity for a single linkage hierarchical clustering algorithm. Lastly,
applications of the proposed method to mixture models involving elec-tricity
distribution data and simulated data are presented.
