A new method for mining maximal frequent itemsets based on graph theory

Mining itemsets plays an important role in all fields of data mining research, such as: association rules, clustering, and classification. Mining all frequent itemsets leads to a massive number of itemsets. This problem can be reduced by finding maximal frequent itemsets (MFI). In this paper, a new method for mining all MFI based on graph theory, is proposed. In the presented method, first, a square matrix corresponding to the transaction elements of database is formed. Then the graph of this matrix is considered and its maximal complete subgraphs (maximal cliques) which are in one-to-one correspondence with MFI are found. Experimental results verify the advantages of the proposed method including: efficiency, simplicity, accuracy, reasonable time and memory space. Moreover, the presented method has good performance in the case of large databases.