01988nas a2200265   4500000000100000000000100001008004100002260001200043653002600055653002600081653002100107653002000128653002600148100001500174700001800189700002100207700001900228700002000247245006900267856005800336300001200394490000600406520129600412022001401708       2025                            d  c06/202510aClustering Algorithms10aCustomer Segmentation10aMachine Learning10aRetail Analysis10aUnsupervised Learning1 aNur Diyabi1 aDuygu Çakır1 aÖmer Melih Gül1 aTevfik Aytekin1 aSeifedine Kadry00aEvaluating Customer Segmentation Techniques in the Retail Sector  uhttps://www.ijimai.org/journal/bibcite/reference/3582  a175-1900 v93 aIn the current competitive corporate landscape, understanding client preferences and adapting marketing strategies accordingly has become crucial. This study evaluates the effectiveness of four machine learning algorithms (K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Self-Organizing Maps (SOM)) for customer segmentation in the Turkish retail market. Two datasets were analyzed: a large-scale Turkish market sales dataset and a focused marketing campaign dataset. The research employed a comprehensive methodology encompassing data preparation, algorithm application, and performance evaluation using metrics such as the Calinski-Harabasz Index and Davies- Bouldin score. Results indicate that K-Means demonstrated superior performance in terms of interpretability and statistical validity. DBSCAN showed strengths in identifying non-spherical clusters, while GMM and SOM provided more granular segmentation. The findings offer actionable insights for Turkish retailers to optimize marketing strategies and enhance customer relationship management. This study contributes to the field of retail analytics by providing a methodological framework for evaluating customer segmentation techniques in specific market contexts.  a1989-1660