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  Discriminative frequent subgraph mining with optimality guarantees

Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.-P., Smola, A., et al. (2010). Discriminative frequent subgraph mining with optimality guarantees. Statistical Analysis and Data Mining, 3(5), 302-318. doi:10.1002/sam.10084.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BDBA-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6A72-7
Genre: Journal Article

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 Creators:
Thoma, M, Author
Cheng, H, Author
Gretton, A, Author              
Han, J, Author
Kriegel, H-P, Author
Smola, AJ, Author              
Song, L, Author
Yu, PS, Author
Yan, X, Author
Borgwardt, KM1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: 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.

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 Dates: 2010-10
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1002/sam.10084
BibTex Citekey: ThomaCGHKSSYYB2010
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Title: Statistical Analysis and Data Mining
Source Genre: Journal
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Pages: - Volume / Issue: 3 (5) Sequence Number: - Start / End Page: 302 - 318 Identifier: -