Open Access

Unsupervised Learning Framework for Country Clustering Based on Agricultural Import Patterns

https://doi.org/10.55640/ijaair-v03i09-09

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Nguyen Minh Anh ¹ , Tran Quoc Bao ¹ ,

⁴ Faculty of Artificial Intelligence, Vietnam Institute of Digital Technologies, Hanoi, Vietnam

⁴ Department of Computer Science, Saigon University of Emerging Technologies, Ho Chi Minh City, Vietnam

Abstract

The increasing availability of international trade data has created opportunities for applying data-driven analytical techniques to understand global agricultural import behavior. Agricultural imports reflect food demand, economic development, population dynamics, and trade dependencies among countries. Traditional descriptive analyses often fail to reveal hidden structural relationships among nations with similar import characteristics. This study proposes an unsupervised learning framework for country clustering based on agricultural import patterns using clustering and visualization techniques. The framework utilizes agricultural import indicators, including cereals, coffee, and meat imports, to identify homogeneous groups of countries exhibiting comparable trade behaviors. The study integrates data preprocessing, exploratory data analysis, clustering, and visualization into a systematic analytical process. A comprehensive review of machine learning, clustering methodologies, and agricultural analytics literature supports the proposed framework. Findings indicate that unsupervised learning techniques provide meaningful segmentation of countries, enabling policymakers, economists, and agricultural planners to recognize trade dependencies, emerging markets, and strategic partnerships. The study contributes a scalable analytical framework for agricultural trade intelligence and demonstrates the value of clustering-based approaches in international agricultural research.

Keywords

Unsupervised Learning, Country Clustering, Agricultural Imports, K-Means Clustering

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