  A classical problem in grammatical inference is to identify a deterministic
finite automaton (DFA) from a set of positive and negative examples. In this
paper, we address the related - yet seemingly novel - problem of identifying a
set of DFAs from examples that belong to different unknown simple regular
languages. We propose two methods based on compression for clustering the
observed positive examples. We apply our methods to a set of print jobs
submitted to large industrial printers.
