  This paper describes a new approach for learning structures of large Bayesian
networks based on blocks resulting from feature space clustering. This
clustering is obtained using normalized mutual information. And the subsequent
aggregation of blocks is done using classical learning methods except that they
are input with compressed information about combinations of feature values for
each block. Validation of this approach is done for Hill-Climbing as a graph
enumeration algorithm for two score functions: BIC and MI. In this way,
potentially parallelizable block learning can be implemented even for those
score functions that are considered unsuitable for parallelizable learning. The
advantage of the approach is evaluated in terms of speed of work as well as the
accuracy of the found structures.
