一种基于Spark的频繁项集快速挖掘算法

如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长，使用传统算法产生频繁项集的计算代价依然很高.为此，提出一种基于Spark的频繁项集快速挖掘算法（fast mining algorithm of frequent itemset based on spark，Fmafibs），利用位运算速度快的特点，设计了一种新颖的模式增长策略.该算法首先采用位串表达项集，利用位运算来快速生成候选项集；其次，针对超长位串计算效率低的问题，考虑将事务垂直分组处理，将同一事务不同组之间的频繁项集通过连接获得候选项集，最后进行聚合筛选得到最终频繁项集.算法在Spark环境下，以频繁项集挖掘领域基准数据集进行实验验证.实验结果表明所提方法在保证挖掘结果准确的同时，有效地提高了挖掘效率.

Fast Mining Algorithm of Frequent Itemset Based on Spark

Improving the efficiency of frequent itemset mining in big data is a hot research topic at present. With the continuous growth of data volume, the computing costs of traditional frequent itemset generation algorithms remain high. Therefore, this study proposes a fast mining algorithm of frequent itemset based on Spark (Fmafibs in short). Taking advantage of bit-wise operation, a novel pattern growth strategy is designed. Firstly, the algorithm converts itemset into BitString and exploits bit-wise operation to generate candidate itemset. Secondly, to improve the processing efficiency of long BitString, a vertical grouping strategy is designed and the candidate itemset are obtained by joining the frequent itemset between different groups of same transaction, and then aggregating and filtering them to get the final frequent itemset. Fmafibs is implemented in Spark environment. The experimental results on benchmark datasets show that the proposed method is correct and it can significantly improve the mining efficiency.