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布尔矩阵Apriori算法的MapReduce并行化实现
引用本文:陈方健,张明新,杨昆.布尔矩阵Apriori算法的MapReduce并行化实现[J].常熟理工学院学报,2014(2):98-101,106.
作者姓名:陈方健  张明新  杨昆
作者单位:[1]苏州大学,计算机科学与技术学院,江苏苏州215006 [2]常熟理工学院,计算机科学与工程学院,江苏常熟215500 [3]中国矿业大学,计算机科学与技术学院,江苏徐州221116
摘    要:提出基于云计算平台(以Hadoop为例)应用布尔矩阵Apriori算法进行大数据关联规则挖掘的MR_B_Apriori算法。将Hadoop平台与布尔矩阵Apriori算法相结合,利用MapReduce框架分块处理布尔矩阵,计算出分块数据的频度,合并融合得到大数据集的频繁项集。分析表明MR_B_Apriori算法能够适用于大数据的频繁项集挖掘。

关 键 词:大数据  Hadoop  数据挖掘  Apriori算法  关联规则

Parallel Implementation of the Apriori Algorithm of Boolean Matrix Based on MapReduce Programing Mode
CHEN Fang-jian,ZHANG Ming-xin,YANG Kun.Parallel Implementation of the Apriori Algorithm of Boolean Matrix Based on MapReduce Programing Mode[J].Journal of Changshu Institute of Technology,2014(2):98-101,106.
Authors:CHEN Fang-jian  ZHANG Ming-xin  YANG Kun
Institution:1.School of Computer Science and Technology, Soochow University, Suzhou 215005, China;2. School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China;3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 22116, China)
Abstract:Knowledge discovery is the bottleneck of big data applications in the environment of big data at pres-ent. The authors of this paper propose a MR_B_Apriori algorithm for mining association rules in large data, which is the Apriori algorithm of Boolean matrix based on cloud computing platform (such as Hadoop). Hadoop platform is combined with Apriori algorithm of Boolean matrix to process blocks of Boolean matrix and to calcu-late the frequency of the block by using MapReduce, and to obtain frequent itemsets of big data by means of combination and integration. The analysis shows that the MR_B_Apriori algorithm can be applied to frequent itemset mining for big data.
Keywords:big data  Hadoop  data mining  Apriori algorithm  association rules
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