Objective To find the meaningful association rules among the unqualified items of the test data, and to analyze and interpret the association rules, further discover the value of the food sampling data, so as to have a certain guiding significance for the food safety supervision. Methods In this paper, the association rules of the unqualified items of the food safety sampling inspection data published on the website of Shandong food and drug administration from 2015 to 2019 were mined by using Apriori algorithm. Results Through the excavation, we got 10 rules that most meet the requirements. Conclusion Using association rules mining algorithm to mine the food inspection data, we can mine the valuable and meaningful rules, which has the guiding significance to the food safety management. From this, we can see that data mining technology in food safety data mining analysis has a broad application prospect.
标题：基于Apriori算法的食品抽检数据的关联规则挖掘
英文标题：Mining association rules of food sampling data based on Apriori algorithms
