Unsupervised NN and graph matching approach to compare data sets

We describe a technique to compare two data partitions of two different data sets as frequently occurs in defect detection. The comparison is obtained dividing each data set in partitions by means of an unsupervised neural network and associating an undirected complete weighted graph structure to these partitions. Then, a graph matching operation returns an estimation of the level of similarity between the data sets.