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This paper proposes an unsupervised classification method that partitions aset of files into non-overlapping dialects based upon their behaviors,determined by messages produced by a collection of programs that consume them.The pattern of messages can be used as the signature of a particular kind ofbehavior, with the understanding that some messages are likely to co-occur,while others are not. Patterns of messages can be used to classify files intodialects. A dialect is defined by a subset of messages, called the requiredmessages. Once files are conditioned upon dialect and its required messages,the remaining messages are statistically independent.  With this definition of dialect in hand, we present a greedy algorithm thatdeduces candidate dialects from a dataset consisting of a matrix offile-message data, demonstrate its performance on several file formats, andprove conditions under which it is optimal. We show that an analyst needs toconsider fewer dialects than distinct message patterns, which reduces theircognitive load when studying a complex format.

Unsupervised clustering of file dialects according to monotonic decompositions of mixtures