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  Near-optimal supervised feature selection among frequent subgraphs

Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.-P., Smola, A., et al. (2009). Near-optimal supervised feature selection among frequent subgraphs. In H. Park, S. Parthasarathy, & H. Liu (Eds.), 9th SIAM Conference on Data Mining (SDM 2009) (pp. 1076-1087). Society for Industrial and Applied Mathematics: Philadelphia, PA, USA.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C4FD-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-FE08-8
Genre: Conference Paper

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 Creators:
Thoma, M, Author
Cheng, H, Author
Gretton, A1, 2, Author              
Han, J, Author
Kriegel, H-P, Author
Smola, AJ, Author              
Song, L, Author
Yu, PS, Author
Yan, X, Author
Borgwardt, KM2, 3, 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              
3Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              

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 Abstract: Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and anomaly detection in program flows. Among the various approaches proposed in the literature, graph classification based on frequent subgraphs is a popular branch: Graphs are represented as (usually binary) vectors, with components indicating whether a graph contains a particular subgraph that is frequent across the dataset. On large graphs, however, one faces the enormous problem that the number of these frequent subgraphs may grow exponentially with the size of the graphs, but only few of them possess enough discriminative power to make them useful for graph classification. Efficient and discriminative feature selection among frequent subgraphs is hence a key challenge for graph mining. 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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Language(s):
 Dates: 2009-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1137/1.9781611972795.92
BibTex Citekey: 5666
 Degree: -

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Title: 9th SIAM Conference on Data Mining (SDM 2009)
Place of Event: Sparks, NV, USA
Start-/End Date: 2009-04-30 - 2009-05-02

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Title: 9th SIAM Conference on Data Mining (SDM 2009)
Source Genre: Proceedings
 Creator(s):
Park, H, Editor
Parthasarathy, S, Editor
Liu, H, Editor
Affiliations:
-
Publ. Info: Society for Industrial and Applied Mathematics : Philadelphia, PA, USA
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1076 - 1087 Identifier: ISBN: 978-1-615-67109-0