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Research on Apriori Algorithm Optimization of Cloud Computing and Big Data in Software Engineering

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DOI: 10.25236/iceeecs.2018.013


Wang Rui

Corresponding Author

Wang Rui


With the continuous expansion of data information, it is more and more difficult to extract effective data information from data analysis. Traditional data analysis algorithms can no longer meet the needs of big data analysis. The rise of Cloud computing provides a new solution to this problem. This paper studies the Cloud computing technology and data mining and analyses the Apriori algorithm. Based on its limitations, it proposes an optimization scheme and introduces the MapReduce model in Cloud computing to achieve parallelization. A MapReduce-based frequent item set mining method is proposed to improve the efficiency of the algorithm and reduce the overhead required for algorithm execution.


Cloud computing, data mining, Apriori algorithm, MapReduce, frequent item sets.