Information Retrieval Systems use clustering techniques to improve the organization and access to documents. K-Means is widely employed in these environments due to its simplicity and effectiveness, but its high computational cost limits scalability. This work evaluates an optimization of the K-Means algorithm that significantly reduces the number of calculations without affecting clustering quality. Building on previous research, the analysis is extended by incorporating new evaluation metrics, such as silhouette, the Davies-Bouldin index, the Dunn index, and inertia, to study the relationship between computational savings and clustering quality. Experiments conducted in different dimensional configurations confirm that the optimization maintains high clustering quality while reducing computational cost. Additionally, it is observed that the reduction in calculations is directly related to the stability of clusters and the dispersion of centroids, validating the effectiveness of the proposed approach.