An efficient mining of transactional data using graph-based technique

Mining association rules is an essential task for knowledge discovery. Past transaction data can be analyzed to discover customer behaviors such that the quality of business decision can be improved. The approach of mining association rules focuses on discovering large itemsets, which are groups of items that appear together in an adequate number of transactions. In this paper, we propose a graph-based approach (DGARM) to generate Boolean association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large itemsets. Practical evaluations show that the proposed algorithm outperforms other algorithms which need to make multiple passes over the database.