## Abstract

We present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. This classifier is based on a local decomposition of the graph for each node in generalized trees. The obtained trees, forming the tree set of the graph, are then pairwise compared by a generalized tree-similarity-algorithm (GTSA) and the resulting similarity scores determine a characteristic similarity distribution of the graph. Classification in this context is defined as mutual consistency for all pure and mixed tree sets and their resulting similarity distributions in a graph class. We demonstrate the application of this method to an artificially generated data set and for data from microarray experiments of cervical cancer.

Original language | English |
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Title of host publication | Proceedings of the 2005 International Conference on Data Mining, DMIN'05 |

Pages | 200-207 |

Number of pages | 8 |

Publication status | Published - 2005 |

Externally published | Yes |

Publication type | A4 Article in conference proceedings |

Event | 2005 International Conference on Data Mining, DMIN'05 - Las Vegas, NV, United States Duration: 20 Jun 2005 → 23 Jun 2005 |

### Conference

Conference | 2005 International Conference on Data Mining, DMIN'05 |
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Country/Territory | United States |

City | Las Vegas, NV |

Period | 20/06/05 → 23/06/05 |

## ASJC Scopus subject areas

- Computer Science Applications
- Software