Motivated by an application to the clustering of milking kinetics of dairy goats, we propose a novel approach for functional data clustering. This issue is of growing interest in precision livestock farming, which is 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 the 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 experiments. Our technique is finally applied to milk emission kinetics data with the aim of a better characterization of interanimal variability and towards a better understanding of the lactation process.

中文翻译：

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一种用于功能数据聚类的新颖正则化方法：在奶山羊挤奶动力学中的应用

通过对奶山羊挤奶动力学的聚类应用，我们提出了一种新的功能数据聚类方法。这个问题在精确的畜牧业中引起了越来越多的兴趣，这主要是基于数据采集自动化的发展和解释性工具的发展，以利用高通量原始数据并生成表型性状的基准。我们在本文中提出的方法就属于这种情况。我们的方法基于一种新颖的正则化变化点估计方法以及基于k的曲线的分段线性估计-均值算法应用于总结曲线的系数向量。通过数值实验评估了我们方法的统计性能，并将其与现有实验进行了全面比较。我们的技术最终应用于乳汁排放动力学数据，目的是更好地表征动物间的变异性并更好地了解泌乳过程。