Abstract: In order to solve the problems of strong subjectivity and low targeting in sampling decision-making that exist in food safety surveillance sampling, this study proposed a correlation analysis method based on an improved Frequent Pattern-growth (FP-growth) algorithm for food risk factors. First, the entropy weight method was used to assign weights to the risk indicators of food categories so as to calculate the risk indices of different food categories. Second, the risk index was used as a feature for risk clustering based on MiniBatchKmeans to obtain the risk level of food products. Finally, an improved FP-growth algorithm with constraints was used for association rule mining of food risk factors to excavate the association relationship between the risk level of food products and the information of food types, time, and geographic attributes, and the mined results were analyzed by correlation analysis so as to provide guidance for precise targeting to guide the decision making of sampling inspection. This study was based on food sampling data from certain regions of China in 2019, which were assigned with indicators to calculate the risk index. Afterwards, the risk was clustered into low (L), medium (M), and high risk (H). Finally, the data was imported into the improved FP-growth algorithm to obtain the association rules of food risk factors. For 17 214 pieces of sampling data, the improved FP-growth algorithm had a shorter running time when compared with the Apriori algorithm. Compared with the traditional one, the improved FP-growth algorithm removed invalid rules and improved the analysis efficiency of the association rules of food risk factors. Thus, it provides an accurate and efficient decision-making basis for the sampling work of food regulatory authorities.

Key words: food safety surveillance sampling; association analysis; entropy weight method; MinibatchKmeans clustering; frequent pattern-growth algorithm