Mining Intervals of Graphs to Extract Characteristic Reaction Patterns
The article introduces an original problem of knowledge discovery from chemical reaction databases that consists in identifying the subset of atoms and bonds that play an effective role in a given chemical reaction. The extraction of the resulting characteristic reaction pattern is then reduced to a graph-mining problem: given lower and upper bound graphs gl and gu, the search of best patterns in an interval of graphs consists in finding among connected graphs isomorphic to a subgraph of gu and containing a subgraph isomorphic to gl, best patterns that maximize a scoring function and whose score depends on the frequency of the pattern in a set of examples. A method called CrackReac is then proposed to extract best patterns from intervals of graphs. Accuracy and scalability of the method are then evaluated by testing the method on the extraction of characteristic patterns from reaction databases.
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