Abstract
Cloud computing is large scale and highly scalable. The data mining based on cloud computing was a very important field. The paper proposed the algorithm of mining frequent itemsets based on mapReduce, namely MFIM algorithm. MFIM algorithm distributed data according horizontal projection method. MFIM algorithm made nodes compute local frequent itemsets with by FP-tree and mapReduce, then the center node exchanged data with other nodes and combined; finally, global frequent itemsets were gained by mapReduce. Theoretical analysis and experimental results suggest that MFIM algorithm is fast and effective.
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References
Chen, Z.B., Han, H., Wang, J.X.: Data Warehouse and Data Mining. Tsinghua University Press, Beijing (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining frequent itemsets. In: Proceedings of the 20th International Conference Very Large Data Base, Santiago, pp. 487–499 (1994)
Park, J.S., Chen, M.S., Yu, P.S.: Efficient distributed data mining for frequent itemsets. In: Proceedings of the 4th International Conference on Information and Knowledge Management, Baltimore, pp. 31–36 (1995)
Agrawal, R., Shafer, J.C.: Distributed mining of frequent itemsets. IEEE Trans. Knowl. Data Eng. 8(6), 962–969 (1996)
Cheung, D.W., Han, J.W., Ng, W.T., Tu, Y.J.: A fast distributed algorithm for mining association rules. In: Proceedings of IEEE 4th International Conference on Management of Data, Miami Beach, pp. 31–34 (1996)
He, B.: Fast mining of global maximum frequent itemsets in distributed database. Control Decis. 26(8), 1214–1218 (2011). (in Chinese with English abstract)
Acknowledgments
This research is supported by the fundamental and advanced research projects of Chongqing under grant No. CSTC2013JCYJA40039 and the science and technology research projects of Chongqing Board of Education under grant No. KJ130825. This research is also supported by the Nanjing university state key laboratory for novel Software technology fund under grant No. KFKT2013B23 and the Shenzhen key laboratory for high-performance data mining with Shenzhen new industry development fund under grant No. CXB201005250021A.
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He, B. (2014). The Algorithm of Mining Frequent Itemsets Based on MapReduce. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Advances in Intelligent Systems and Computing, vol 250. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1695-7_62
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DOI: https://doi.org/10.1007/978-81-322-1695-7_62
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