A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats

Christophe Denis (LAMA, Université Paris-Est Marne-la-Vallée)

26 mars 2021

Motivated by an application to the clustering of milking kinetics of dairygoats, we propose in this talk 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 falls in this context. Our methodology relies on a piecewise linear estimation of curves based on a novel regularized change-point estimation method. Our technique is 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.;;