Frequent itemset mining (FIM) is a highly computational and data intensive algorithm. Therefore, parallel and distributed FIM algorithms have been designed to process large volume of data in a reduced time. Recently, a number of FIM algorithms have been designed on Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for the highly iterative FIM algorithms. Therefore, Spark, a more efficient distributed data processing framework, has been developed with in-memory computation and resilient distributed dataset (RDD) features to support the iterative algorithms. On this framework, Apriori and FP-Growth based FIM algorithms have been designed on the Spark RDD framework, but Eclat-based algorithm has not been explored yet. In this paper, RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework is proposed with its five variants. The proposed algorithms are evaluated on the various benchmark datasets, and the experimental results show that RDD-Eclat outperforms the Spark-based Apriori by many times. Also, the experimental results show the scalability of the proposed algorithms on increasing the number of cores and size of the dataset.

本研究提出了RDD-Eclat并行算法来支持更高效的大数据处理，实验结果表明RDD-Eclat相对于Spark-based Apriori具有更快的处理速度，同时还展示了该算法在多核处理和数据集递增方面的可伸缩性。

RDD-Eclat: 基于Spark RDD框架并行化Eclat算法的方法(扩展版)