  Motivated by an application to the clustering of milking kinetics of dairy
goats, we propose in this paper a novel approach for functional data
clustering. This issue is of growing interest in precision livestock farming
that has been largely based on the development of data acquisition automation
and on the development of interpretative tools to capitalize on high-throughput
raw data and to generate benchmarks for phenotypic traits. The method that we
propose in this paper falls in this context. Our methodology relies on a
piecewise linear estimation of curves based on a novel regularized change-point
estimation method and on the k-means algorithm applied to a vector of
coefficients summarizing the curves. The statistical performance of our method
is assessed through numerical experiments and is thoroughly compared with
existing ones. Our technique is finally applied to milk emission kinetics data
with the aim of a better characterization of inter-animal variability and
toward a better understanding of the lactation process.
