de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Discriminative frequent subgraph mining with optimality guarantees

MPS-Authors
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;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BDBA-9
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.