Header logo is ei

Discriminative frequent subgraph mining with optimality guarantees

2010

Article

ei


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.

Author(s): Thoma, M. and Cheng, H. and Gretton, A. and Han, J. and Kriegel, H-P. and Smola, AJ. and Song, L. and Yu, PS. and Yan, X. and Borgwardt, KM.
Journal: Journal of Statistical Analysis and Data Mining
Volume: 3
Number (issue): 5
Pages: 302–318
Year: 2010
Month: October
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1002/sam.10084

Links: Web

BibTex

@article{ThomaCGHKSSYYB2010,
  title = {Discriminative frequent subgraph mining with optimality guarantees},
  author = {Thoma, M. and Cheng, H. and Gretton, A. and Han, J. and Kriegel, H-P. and Smola, AJ. and Song, L. and Yu, PS. and Yan, X. and Borgwardt, KM.},
  journal = {Journal of  Statistical Analysis and Data Mining},
  volume = {3},
  number = {5},
  pages = {302–318},
  month = oct,
  year = {2010},
  month_numeric = {10}
}