It is my understanding that the CH criterion is primarily meant to compare the impact of changing the value of k for a given clustering method, not to compare two different methods with the same value of k. If the goal of this example is to compare the quality of the clustering obtained by k-means and SOM I would rather use standard supervised metrics such as `sklearn.metrics.cluster.adjusted_rand_score` and the like as done in other clustering examples and use the `digits.target` as the ground truth.