This work is devoted to the problem of clustering a set of samples according to the effect they have as one of the many varying inputs of a model. An example is the problem of clustering weather series according to the effect they have on the yield simulated with a crop model when also other inputs such as soil or plant parameters vary. We introduce both simple solutions based on the K-Means algorithms and the derivation of two possible formulations of the clustering problem in a sensitivity analysis framework. We show that these formulations coincide in the sense that their criteria are closely related to each other, leading to a possibility to use the K-Means algorithm with an improved expression of clustering performance through the use of Sobol' indices.
Roux et al. (Mon,) studied this question.