Comparison of K-Means and Hierarchical Clustering Methods for Buffalo Milk Production Data.

ABSTRACT: This study investigated the use of K-means and hierarchical clustering, to group Italian Mediterranean buffalo using routinely collected test-day records. The analysis was first conducted on a combined dataset comprising three buffalo herds and subsequently on each herd individually. The main objective was to determine whether data-driven groupings could be implemented to support improvements in general herd management strategies. Results indicated that K-means consistently outperformed hierarchical clustering across all datasets, as reflected by average silhouette scores (0.17-0.18 vs. 0.10-0.12 for K-means and hierarchical, respectively), favorable Davies-Bouldin Index (DBI; 2.05-2.16 vs. 2.11-2.5 for K-means and hierarchical, respectively) and Calinski-Harabasz Index values (CHI; 1034-3

SUBMITTER: Trapanese L 

PROVIDER: S-EPMC12649153 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature