<![CDATA[The fashion retail industry generates large volumes of sales transaction data containing valuable information regarding consumer purchasing behavior and preferences. However, extracting meaningful insights from heterogeneous retail data remains challenging when using conventional analytical approaches. This study aims to analyze jeans sales transaction data and identify consumer purchasing patterns using the Fuzzy C-Means (FCM) clustering algorithm. The proposed approach adopts the Knowledge Discovery in Databases (KDD) framework, consisting of data selection, preprocessing, transformation, data mining, and evaluation stages to ensure systematic analysis. The dataset used in this study consists of 799 jeans sales transaction records collected in 2024 from Shakila Collection, involving four attributes: product name, payment method, price, and purchase quantity. To improve clustering effectiveness, only price and purchase quantity were selected as the primary variables due to their relevance in representing consumer purchasing behavior. Clustering performance was evaluated using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. Experimental results show that the best clustering configuration was achieved at , producing three consumer segments consisting of 175 items in Cluster 0, 590 items in Cluster 1, and 34 items in Cluster 2. The findings indicate that medium-priced products tend to have higher purchasing intensity and more flexible purchase quantities, whereas premium-priced products exhibit relatively lower demand. The novelty of this study lies in integrating Fuzzy C-Means clustering with consumer preference analysis to generate practical business insights for pricing strategies, inventory optimization, and targeted marketing, thereby supporting more effective data-driven decision-making in fashion retail businesses.]]>
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