We compare the results of splitting batches of industrial products (semiconductor devices) into several prospective homogeneous production batches using the standard k-means and p-median clustering models with various normalization methods. In the case of clustering problems, quadratic Euclidean distances are the most popular. We use them as well as the Mahalanobis distances to calculate the differences (distances) in the normalized space of the industrial product features, and compare the clustering results with the use of the Rand index, and empirically establish the advantage of the p-median model.