布尔矩阵Apriori算法的MapReduce并行化实现

Parallel Implementation of the Apriori Algorithm of Boolean Matrix Based on MapReduce Programing Mode

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.

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.