Research on Apriori algorithm based on compression processing and hash table

Chengqi Yu; Ying Liang; Xinxin Zhang

doi:10.1117/12.2675260

25 May 2023 Research on Apriori algorithm based on compression processing and hash table

Chengqi Yu, Ying Liang, Xinxin Zhang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126362I (2023) https://doi.org/10.1117/12.2675260
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Apriori improved algorithm based on compression processing and hash table is given to solve the problem that the traditional Apriori (association rules) algorithm must repeatedly scan the database when performing data mining frequent itemsets. It makes full use of the method of transaction compression, firstly compressing the data, and then compressing the obtained data into the hash table for the second time through hashing technology. In this process, you only need to scan the database once, and then even if the support degree changes, there is no need to perform a second scan. The experimental results show that the improved algorithm can not only mine the association rules of the dataset correctly, but also greatly improve the operation efficiency of the algorithm.

Citation Download Citation

Chengqi Yu, Ying Liang, and Xinxin Zhang "Research on Apriori algorithm based on compression processing and hash table", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126362I (25 May 2023); https://doi.org/10.1117/12.2675260

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