BACKGROUND: Foods are not purchased in isolation but are normally co-purchased with other food products. The patterns of co-purchasing associations across a large number of food products have been rarely explored to date. Knowledge of such co-purchasing patterns will help evaluate nutrition interventions that might affect the purchasing of multiple food items while providing insights about food marketing activities that target multiple food items simultaneously. OBJECTIVE: To quantify the association of food products purchased with each of three food categories of public health importance: soda, fresh fruits and fresh vegetables using Association Rule Mining (ARM) followed by longitudinal regression analysis. METHODS: We obtained transaction data containing grocery purchasing baskets (lists of purchased products) collected from loyalty club members in a major supermarket chain between 2015 and 2017 in Montréal, Canada. There were 72 food groups in these data. ARM was applied to identify food categories co-purchased with soda, fresh fruits, and fresh vegetables. A subset of co-purchasing associations identified by ARM was further tested by confirmatory logistic regression models controlling for potential confounders of the associations and correlated purchasing patterns within shoppers. RESULTS: We analyzed 1,692,716 baskets. Salty snacks showed the strongest co-purchasing association with soda (Relative Risk [RR] = 2.07, 95% Confidence Interval [CI]: 2.06, 2.09). Sweet snacks/candies (RR = 1.73, 95%CI: 1.72-1.74) and juices/drinks (RR:1.71, 95%CI:1.71-1.73) also showed strong co-purchasing associations with soda. Fresh vegetables and fruits showed considerably different patterns of co-purchasing associations from those of soda, with pre-made salad and stir fry showing a strong association (RR = 3.78, 95% CI:3.74-3.82 for fresh vegetables and RR = 2.79, 95%CI:2.76-2.81 for fresh fruits). The longitudinal regression analysis confirmed these associations after adjustment for the confounders, although the associations were weaker in magnitude. CONCLUSIONS: Quantifying the interdependence of food products within shopping baskets provides novel insights for developing nutrition surveillance and interventions targeting multiple food categories while motivating research to identify drivers of such co-purchasing. ARM is a useful analytical approach to identify such cross-food associations from retail transaction data when combined with confirmatory regression analysis to adjust for confounders of such associations.