RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework
This work addresses performance bottlenecks in distributed data mining for big data applications, 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 Hadoop MapReduce by proposing RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework, which outperforms Spark-based Apriori by many times and shows scalability with increasing cores and dataset size.
Initially, a number of frequent itemset mining (FIM) algorithms have been designed on the Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for such highly iterative 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 the Spark RDD framework, Apriori and FP-Growth based FIM algorithms have been designed, 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, which shows 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.