Research on an improved algorithm of Apriori based on Hadoop
Hongqin Wang, Jiang Huiyong, Hongxia Wang, Lina Yuan · 2020
With the development of mobile Internet, association rules data mining is still a research hotspot. In this paper, the traditional mining algorithm of association rules for Apriori is analyzed, which has low efficiency and poor expansibility, because it scans the database many times and produces a large number of redundant frequent itemsets when dealing with big data. Therefore, it is proposed that the Apriori algorithm is improved by using the MapReduce model of Hadoop platform to parallelize processing, and the experimental results show that the improved Apriori algorithm has high efficiency and good stability in big data processing, it has great potential to excavate. Finally, the algorithm is applied to the data mining of student scores, which verifies its effectiveness and can provide services for education management.