  In this paper, we develop a statistical methodology applied to the
characterization of flowering curves using Gaussian mixture models. Our study
relies on a set of rosebushes flowering data, and Gaussian mixture models are
mainly used to quantify the reblooming properties of each one. In this regard,
we also suggest our own selection criterion to take into account the lack of
symmetry of most of the flowering curves. Three classes are created on the
basis of a principal component analysis conducted on a set of reblooming
indicators, and a subclassification is made using a longitudinal $k$--means
algorithm which also highlights the role played by the precocity of the
flowering. In this way, we obtain an overview of the correlations between the
features we decided to retain on each curve. In particular, results suggest the
lack of correlation between reblooming and flowering precocity. The pertinent
indicators obtained in this study will be a first step towards the
comprehension of the environmental and genetic control of these biological
processes.
