A copula-based clustering algorithm to analyse EU country diets
Authors: Di Lascio, F.M.L. and Disegna, M.
Journal: Knowledge-Based Systems
Volume: 132
Pages: 72-84
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2017.06.004
Abstract: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.
https://eprints.bournemouth.ac.uk/29318/
Source: Scopus
A copula-based clustering algorithm to analyse EU country diets
Authors: Di Lascio, F.M.L. and Disegna, M.
Journal: Knowledge-Based Systems
Volume: 132
Pages: 72-84
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2017.06.004
Abstract: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.
https://eprints.bournemouth.ac.uk/29318/
Source: Manual
A copula-based clustering algorithm to analyse EU country diets
Authors: Di Lascio, F.M.L. and Disegna, M.
Journal: Knowledge-Based Systems
Volume: 132
Issue: September
Pages: 72-84
ISSN: 0950-7051
Abstract: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.
https://eprints.bournemouth.ac.uk/29318/
Source: BURO EPrints