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  2012, Vol. 25 Issue (2): 220-224    DOI:
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Closed Frequent Itemset Mining Based on MapReduce
CHEN Guang-Peng, YANG Yu-Bin, GAO Yang, SHANG Lin
State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093

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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.
Key wordsCloud Computing      Parallel Algorithm      Data Mining      Closed Frequent Itemset      MapReduc     
Received: 14 February 2011     
ZTFLH: TP311  
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CHEN Guang-Peng
YANG Yu-Bin
GAO Yang
SHANG Lin
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CHEN Guang-Peng,YANG Yu-Bin,GAO Yang等. Closed Frequent Itemset Mining Based on MapReduce[J]. , 2012, 25(2): 220-224.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I2/220
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