Abstract：Closed frequent itemset mining is an useful way for discovering association rules from data. Cloud computing infrastructure based on MapReduce provides a promising solution to address the problem. A parallel algorithm for mining closed frequent itemset is presented based on the Hadoop cloud computing platform. The method consists of four steps: parallel counting, global F-List constructing, parallel mining of local closed frequent itemset and parallel filtrating of global closed frequent itemset. The experimental results validate the method and show that it is effective with a satisfied speedup.
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2012, Vol. 25 
