@InProceedings{Supelec431,

author = {Frederic Pennerath and GĂ©raldine Polaillon and Amedeo Napoli},

title = {Mining Intervals of Graphs to Extract Characteristic Reaction Patterns},

year = {2008},

booktitle = {Discovery Science, 11th International Conference, DS 2008 Proceedings.},

publisher = {Springer-Verlag},

volume = {5255},

pages = {210-221},

month = {Oct 13-16},

editor = {Boulicaut J.-F. and Berthold M.R. and Horvŕrth T.},

series = {Lecture Notes in Artificial Intelligence},

address = {Budapest (Hungary)},

url = {http://dx.doi.org/10.1007/978-3-540-88411-8_21},

doi = {10.1007/978-3-540-88411-8_21},

abstract = {The article introduces an original problem of knowledge discovery
from chemical reaction databases that is closely related to
reaction clustering. The problem aims at identifying the subset
of atoms and bonds that play an effective role in a given
chemical reaction. The resulting characteristic reaction pattern
describes the synthesis method underlying the reaction. Solution
to this question is approached by a graph-mining optimization
problem whose setting is new: given lower and upper bound graphs
gl and gu, the search of best patterns in an interval of graphs
consists in finding among graphs isomorphic to a subgraph of gu
and containing a subgraph isomorphic to gl, best patterns that
maximize a scoring function. Score values depend on the frequency
of patterns in a set of examples.
The article also studies accuracy and scalability of the method
when applied to reaction databases.
}

}