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Discriminative frequent subgraph mining with optimality guarantees

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Cheng H, Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84953

Han J, Kriegel H-P, Smola,  AJ
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Thoma, M., Cheng H, Gretton, A., Han J, Kriegel H-P, Smola, A., Song L, Yu PS, Yan, X., & Borgwardt, K. (2010). Discriminative frequent subgraph mining with optimality guarantees. Statistical Analysis and Data Mining, 3(5), 302–318. doi:10.1002/sam.10084.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BDBA-9
Zusammenfassung
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near-optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining.