An efficient graph-based approach to mining association rules for large databases. (13th August 2009) - Huang, Lee-Wen; Chang, Ye-In
The task of data mining is to find the useful information within the incredible sets of data. One of important research areas of data mining is mining association rules. If we can find these relations by mining association rules, we can provide better selling strategy to gain more customers' attentions. However, in some applications, the large itemsets may not always correlate with each other. In this paper, we propose a new graph-based algorithm to discover the association rules. It represents the large itemsets as a graph, which constructs a graph based on L2. Then, by dividing the items to several groups, the association rule can be mined efficiently. We conduct several experiments using different synthetic transaction databases. The simulation results show that the GAR algorithm outperforms the FP-growth algorithm in the execution time for all transaction databases.