  With the aim to propose a non parametric hypothesis test, this paper carries
out a study on the Matching Error (ME), a comparison index of two partitions
obtained from the same data set, using for example two clustering methods. This
index is related to the misclassifica-tion error in supervised learning. Some
properties of the ME and, especially, its distribution function for the case of
two independent partitions are analyzed. Extensive simulations show the
efficiency of the ME and we propose a hypothesis test based on it.
