Mining Frequent Connected Subgraphs Reducing the Number of Candidates
Abstract
In this paper, a new algorithm for mining frequent connected subgraphs called gRed ( g raph Candidate Red uction Miner) is presented. This algorithm is based on the gSpan algorithm proposed by Yan and Jan. In this method, the mining process is optimized introducing new heuristics to reduce the number of candidates. The performance of gRed is compared against two of the most popular and efficient algorithms available in the literature (gSpan and Gaston). The experimentation on real world databases shows the performance of our proposal overcoming gSpan, and achieving better performance than Gaston for low minimal support when databases are large.
Cite
Text
Alonso et al. "Mining Frequent Connected Subgraphs Reducing the Number of Candidates." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87479-9_42Markdown
[Alonso et al. "Mining Frequent Connected Subgraphs Reducing the Number of Candidates." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/alonso2008ecmlpkdd-mining/) doi:10.1007/978-3-540-87479-9_42BibTeX
@inproceedings{alonso2008ecmlpkdd-mining,
title = {{Mining Frequent Connected Subgraphs Reducing the Number of Candidates}},
author = {Alonso, Andrés Gago and Medina-Pagola, José Eladio and Carrasco-Ochoa, Jesús Ariel and Trinidad, José Francisco Martínez},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {365-376},
doi = {10.1007/978-3-540-87479-9_42},
url = {https://mlanthology.org/ecmlpkdd/2008/alonso2008ecmlpkdd-mining/}
}