<![CDATA[The increasing availability of transactional data in the retail sector provides opportunities to support data-driven managerial decision-making. This study aims to segment supermarket sales transactions using the K-Means clustering method to identify meaningful transaction patterns that support retail decision-making. A publicly available supermarket transaction dataset was analyzed using selected numerical attributes representing purchase quantity, transaction value, and customer rating. To ensure reliable and interpretable clustering results, data standardization was applied, and the optimal number of clusters was determined using a combined validation strategy comprising the Elbow Method and the Silhouette Score. The results indicate that three distinct transaction segments were identified, characterized by similar purchase quantities but differing transaction values and customer satisfaction levels. Principal Component Analysis visualization confirms that the resulting clusters are well separated and interpretable. The findings demonstrate that integrating systematic cluster validation with interpretable cluster analysis provides practical insights for retail managers in designing targeted marketing strategies, improving customer satisfaction, and supporting inventory and promotional decision-making.]]>
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