### Abstract

In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected edges in a subgraph. This measure is adapted from spatial scan statistics for point sets and provides quantitative assessment for clusters. We discuss some important properties of this statistic and its relation to modularity and Bregman divergences. We apply a simple clustering algorithm to find clusters with large values of this measure in a variety of real-world data sets, and we illustrate its ability to identify statistically significant clusters of selected granularity.

Original language | English (US) |
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Title of host publication | Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130 |

Publisher | Society for Industrial and Applied Mathematics Publications |

Pages | 727-738 |

Number of pages | 12 |

ISBN (Print) | 9781605603179 |

DOIs | |

State | Published - 2008 |

Event | 8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130 - Atlanta, GA, United States Duration: Apr 24 2008 → Apr 26 2008 |

### Publication series

Name | Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130 |
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Volume | 2 |

### Other

Other | 8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130 |
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Country | United States |

City | Atlanta, GA |

Period | 4/24/08 → 4/26/08 |

### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Information Systems
- Software
- Signal Processing

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## Cite this

*Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130*(pp. 727-738). (Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130; Vol. 2). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972788.66