A Parallel Association-Rule Mining Algorithm

摘要

Although the FP-Growth association-rule mining algorithm is more efficient than the Apriori algorithm, it has two disadvantages. The first is that the FP-tree can become too large to be created in memory: the second is the serial processing approach used. In this paper, a kind of parallel association-rule mining algorithm has been proposed. It does not need to create an overall FP-tree, and it can distribute data mining tasks over several computing nodes to achieve parallel processing. This approach will greatly improve efficiency and processing ability when used for mining association rules and is suitable for association-rule mining on massive data sets.