<![CDATA[Purpose – This study explores anthropometric patterns among children under five using Agglomerative Hierarchical Clustering (AHC) with Complete Linkage based on age, body weight, and height. Methods – A quantitative observational design with a data mining approach was employed using 1,000 individual child records from a primary healthcare setting. Age, body weight, and height were statistically standardized before clustering. AHC with Complete Linkage and Euclidean distance was applied, while cluster quality was evaluated using the Silhouette Coefficient for solutions ranging from two to seven clusters. Findings – The two-cluster solution achieved the highest Silhouette Coefficient (0.6325), indicating the strongest internal validation, while the three-cluster solution produced a slightly lower value (0.6254) and was retained for secondary exploratory analysis. The three-cluster configuration comprised a dominant group of 937 observations, a smaller group of 62 observations, and one isolated observation. The two main groups had similar mean ages but differed in body weight and height, indicating heterogeneity in combined anthropometric characteristics. Research implications – Complete Linkage clustering provides an exploratory approach for identifying multidimensional patterns in individual-level anthropometric records. However, the clusters should not be interpreted as clinical or nutritional-status classifications because age- and sex-standardized anthropometric indicators were not used. Originality – This study demonstrates the value of Complete-Linkage AHC for exploring heterogeneity in routine child anthropometric data while clearly distinguishing exploratory clustering from established nutritional assessment.]]>
Copyrights © 2026
