RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework (Extended Version)
This addresses a bottleneck for data miners needing faster processing of large datasets, but it is incremental as it adapts an existing algorithm to a new framework.
The paper tackled the inefficiency of existing frequent itemset mining algorithms on distributed frameworks by proposing RDD-Eclat, a parallel Eclat algorithm on Spark RDD, which outperforms Spark-based Apriori by many times and shows scalability with cores and dataset size.
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.