EU countries clustering for the state of food security using machine learning techniques

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ДВНЗ «Київський національний економічний університет імені Вадима Гетьмана»
The food security problem has emerged from the growing pressure of demographic problem and global inequality. Overall, the state of food security is optimal in the EU. This was achieved due to effective implementation of regulatory initiatives regarding EU countries food self-sufficiency and intra-EU food market protection. The purpose of the research paper was to cluster EU countries in terms of food security level using advanced mathematical modeling tools. To this end, we selected 5 food security factors (FAO Food production index, Total factor productivity in agriculture, Per capita agricultural expenditure, Consumer prices food, Net trade food index) to which we applied the following cluster analysis algorithms (self-organizing maps, dendrograms, k-means and k-medoids clustering). As a result of the conducted experimental research, it was found that self-organizing maps and dendrograms methods to be better suited for data visualization, whereas k-means and k-medoids give more accurate and detailed solutions. The obtained results gave us an opportunity to define the advantages and disadvantages of the selected clustering methods, as well as to present agripolicy recommendations for different groups of EU countries.
cluster analysis, food security, self-organizing map, hierarchical clustering, k-means, k-medoids
Kobets V. EU countries clustering for the state of food security using machine learning techniques / Vitaliy Kobets, Oleksandra Novak // Нейро-нечіткі технології моделювання в економіці : наук.-анал. журн. / М-во освіти і науки України, ДВНЗ «Київ. нац. екон. ун-т ім. Вадима Гетьмана» ; [редкол.: А. В. Матвійчук (голов. ред.) та ін.]. – Київ : КНЕУ, 2021. – № 10. – С. 86–118.