We study the problem of finding frequent structures in semistructured
data (represented as a directed labeled graph). Frequent structures are
graphs that are isomorphic to a large number of subgraphs in the data
graph. Frequent structures form building blocks for visual exploration
and data mining of semistructured data. We overcome the inherent
computational complexity of the problem by using a summary data
structure to prune the search space and to provide interactive feedback.
We present an experimental study of our methods operating on real
datasets. The implementation of our methods is capable of operating on
datasets that are two to three orders of magnitude larger than those
described in prior work.
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