PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient mining of frequent itemsets in social network data based on MapReduce framework
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Efficient algorithms for frequent pattern mining in many-task computing environments
Knowledge-Based Systems
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Cloud computing provides cheap and efficient solutions of storing and analyzing mass data. It is very important to research the data mining strategy based on cloud computing from the theoretical view and practical view. In this paper, the strategy of mining association rules in cloud computing environment is focused on. Firstly, cloud computing, Hadoop, MapReduce programming model, Apriori algorithm and parallel association rule mining algorithm are introduced. Then, a parallel association rule mining strategy adapting to the cloud computing environment is designed. It includes data set division method, data set allocation method, improved Apriori algorithm, and the implementation procedure of the improved Apriori algorithm on MapReduce. Finally, the Hadoop platform is built and the experiment for testing performance of the strategy as well as the improved algorithm has been done. The results show that the strategy designed in this paper can archive higher efficiency when doing frequent item set mining in cloud computing environment.