Research On Algorithms For Frequent Subgraph Mining

Data Mining is the result of which people research and development on database for long-term.It can query,traverse and access the database,find the potential links between the data,thus promotes the generation of useful information.Graph structure can simulate almost all of the links between things, such as web mining,spatial data mining, bioinformatics, protein structure mining, drug design and function prediction and other fields,it has a wide range of applications on them.As an important branch of graph mining,frequent subgraph mining is the basis of graph classification,clustering and other graph mining studies,thus it makes frequent subgraph mining work has more far-reaching significance.The focus of this paper is as follows:Firstly, this paper introduces the definition of data mining and the basic technology which data mining commonly use, introduces the basic knowledge and definition of frequent subgraph mining as focus,introduces several classical algorithms of frequent subgraph mining,and analysis their technology.Secondly, we propose a new algorithm based on Apriori algorithm for frequent subgraph mining named GAI.It improves the representation of graph, the way of judging subgraph isomorphism and the way of calculating the support degree of graph.Then, in this paper, we propose a graph clustering algorithm based on frequent subgraph mining algorithm GAI, also explain how to construct feature sets and how to cluster.Finally, in this paper, we example and experiment the GAI algorithm.The new algorithm for mining frequent subgraphs shows a significant advantage in the run-time efficiency.This experiment also shows the accuracy of the algorithm and experimental studies have shown that through GAI we can find all of the frequent sub-graph models that we need, and GAI is more effective than the previous algorithm AGM and FSG.