A copula-based clustering algorithm to analyse EU country diets - Di Lascio, F.Marta L.; Disegna, Marta (Knowledge-based systems, 2017)
•A novel copula-based clustering algorithm is suggested to group EU countries.•Average calories of different food aggregates are used as segmentation variables.•Complex multivariate associations in Countries dietary structures are identified.•Changes towards a (un)common (un)healthier food dietary structure are investigated.•Countries at risk of an increase in obesity and diet-related disease are identified. The aim of the paper is to explore the evolution of food diets in 40 European countries according to the common European policies and guidelines on healthy diets. To this end, an innovative clustering method, called CoClust, has been adopted. By means of the copula function, this algorithm is able to find clusters based on the complex multivariate dependence structure of the data generating process, overcoming the limits of classical approaches that cope with only linear bivariate relationships. The analysed database contains information on the average calories from 16 food aggregates in 40 European countries observed over 40 years by the Food and Agriculture Organisation of the United Nations (FAO). Our findings suggest that European country diets are changing, individually or as a group, but not in a unique direction. Central and Eastern European countries are becoming unhealthier, while the tendency followed by the majority of the remaining countries is to integrate the common European guidelines on healthy, balanced, and diversified diets in their national policies.