关键词: 频繁子图挖掘, MapReduce, Hadoop平台

Abstract: Frequent subgraph mining is an important problem in data mining domain and has been used widely. This paper proposes an efficient algorithm Cloud-GFSG (cloud-global frequent subgraph), by using MapReduce on Hadoop platform for mining frequent subgraphs. The algorithm is based on the principle of Apriori. It uses the discovered frequent subgraphs whose support is k-1 to generate the candidate frequent subgraphs whose support is k when it generates new subgraphs by extending edge. Meanwhile, it checks whether there exists any subgraph which would be generated and sets the frequent subgraph generation rules to ensure the uniqueness of the frequent subgraphs. Compared with the state-of-the-art algorithms, the proposed algorithm has more general function and can avoid generating replicate graphs while extending a new edge. Therefore, its correctness can be ensured and the efficiency had been improved significantly.

Key words: frequent subgraph mining, MapReduce, Hadoop platform