FSG-based algorithm for mining maximal frequent subgraph
基于FSG的最大频繁子图挖掘算法*

Graph mining has become a hot topic in the field of data mining, however, mining all frequent subgraph is very difficult and will get excessive frequent subgraph this impact on the understanding and application of the outcome. Through mi-ning maximal frequent subgraph to solve the problems of the number of the result is huge. Maximal frequent subgraph mining obtained a small number of the results and this without loss information, mining maximal frequent subgraph saved space and the work of analysis. This paper based on algorithm FSG proposed an algorithm FSG-MaxGraph for mining maximal frequent subgraphs. Combined the degree, nodes and adjacency list to calculating normal matrix coding and proposed two theorems that could reduce the times of subgraph isomorphism this improve the efficiency of the algorithm. Last, used the improved decision tree to computing support. The experiment can prove the new algorithm can solve the problem of the mining results difficult to understand and this new algorithm can improve the efficiency of mining.