<![CDATA[The efficiency of clustering algorithms significantly depends on the initialization quality, especially in unsupervised learning applied to complex datasets. This study introduces an enhanced K-Medoids clustering approach using Z-Score-based medoid initialization to improve convergence speed and cluster validity. The method was evaluated using the QSAR Fish Toxicity dataset, consisting of 908 instances and seven numerical features. Initial medoids were selected based on standardized Z-Score values, resulting in a substantial reduction in convergence time from an average of 6 iterations to just 2. Clustering performance was assessed using three internal validation metrics: Davies-Bouldin Index (DBI), Silhouette Coefficient (SC), and Calinski-Harabasz Index (CHI). The DBI score decreased from 1.7328 to 0.8768, indicating improved cluster compactness and separation. In parallel, the SC increased from 0.327 to 0.619, and the CHI rose from 214.75 to 562.43, confirming more coherent and well-separated clusters. These results demonstrate that Z-Score-based initialization significantly boosts the robustness of K-Medoids, offering a simple yet effective strategy for unsupervised partitioning, particularly in toxicological and biochemical data analysis.]]>
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