Frequent mining of subgraph structures
Ping Guo, Xinru Wang, Yanrong Kang · Journal of Experimental & Theoretical Artificial Intelligence · 2006
In recent years, frequent graph pattern mining has raised great interest in the data mining community and has been applied to bioinformatics, web exploration, etc. The difficulty is that the search of possible subgraph patterns is combinatorially explosive. Many researchers proposed methods to resolve this problem, such as FSG, gSpan, and CloseGraph. But we discovered that the result set generated by these methods contains many repetitive structures. In this article, we propose a new method called Frequent Structure Mining Algorithm (FSMA) to find frequent structure in graph database, and prove corresponding theoretics to guarantee the accuracy of FSMA. The power of FSMA comes from the resolution of finding different structures and the storage of the graph set; it guarantees the completeness of mining results.