Mining Frequent Connected Subgraphs Reducing the Number of Candidates

Authors:
Andrés Gago Alonso;José Eladio Medina Pagola;Jesús Ariel Carrasco-Ochoa;José Fco. Martínez-Trinidad
Affiliations:
Data Mining Departament, Advanced Technologies Application Center (CENATAV), La Habana, Cuba CP: 12200 and National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico CP: 72 ...;Data Mining Departament, Advanced Technologies Application Center (CENATAV), La Habana, Cuba CP: 12200;National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico CP: 72840;National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico CP: 72840
Venue:
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Year:
2008

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Abstract

In this paper, a new algorithm for mining frequent connected subgraphs called gRed (graph Candidate Reduction 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.