Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
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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 patternis then reduced to a graph-mining problem: given lower and upper bound graphs gland gu, the search of best patterns in an interval of graphsconsists in finding among connected graphs isomorphic to a subgraph of guand 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 CrackReacis 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.