Substructure Discovery Using Minimum Description Length and Background Knowledge

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally-bounded inexact graph match that identi es similar, but not identical, instances of a substructure and nds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimumdescription length principle, other background knowledge can be used by Subdue to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate Subdue's ability to nd substructures capable of compressing the original data and to discover structural concepts important to the domain.