An Efficient Parallel Algorithm for Frequent Itemsets Mining Using BitTable on Spark
A variety of techniques have been used to improve the performance of an algorithm in finding frequent item sets, which is one of the important processes to obtain frequent pattern mining. It was found that today’s technology has resulted in an ever-increasing amount of information, which should be analyzed for various benefits. Therefore, efforts have been made to improve the aalgoruthm’s efficiency to accommodate the nature of data stored through the working process of the main internal memory. Efforts have been made to prepare algorithms for the ever-increasing information. This research provided an appropriate data structure of BitTable to help improve the functionality of the algorithms. Moreover, the principle of parallel frequent itemset mining algorithm based on Map-Reduce design was used in this research to assess the performance of algorithms, named as Adaptive Hybrid Parallel Algorithm (AHP). Additionally, to investigate the performance of the AHP Algorithm Using Apache Spark Technology with the type of data that was accumulated during the process of the main internal memory.
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