A Fast Frequent Subgraph Mining Algorithm
Jia Xin Wu, Ling Chen · 2008
An algorithm for mining frequent subgraphs in large database of labeled graphs is proposed. The algorithm uses incidence matrix to represent the labeled graphs and to detect their isomorphism. Starting from the frequent edges from the graph database, the algorithm searches the frequent subgraphs by adding frequent edges progressively. By normalizing the incidence matrix of the graph, the algorithm can effectively reduce the computational cost on verifying the isomorphism of the subgraphs. Experimental results show that the algorithm has higher speed and efficiency than that of other similar ones.