Xuexian Qiu
ABSTRACT
In data mining, association rule mining has been proven to be a valuable strategy. The Apriori algorithm, proposed in 1993, is one of the most classic association rule mining algorithms and has been widely used in various domains to date. However, the algorithm itself requires multiple scans of the dataset, making the efficiency improvement of the algorithm an important topic. In this study, focusing on the association rule mining problem in shopping basket datasets, we address the efficiency and accuracy issues of the traditional Apriori algorithm in this domain and propose a novel improved Apriori algorithm. This algorithm enhances the accuracy and time efficiency of rule mining by introducing deletion and identification modules that exploit the characteristics of shopping basket datasets. Through mathematical reasoning and experimental validation, it is demonstrated that the improved Apriori algorithm achieves higher accuracy and time efficiency, thus proving its superior
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Index Terms
- Research and Application of an Improved Apriori Algorithm in Market Basket Data
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