<![CDATA[Tight competition and product diversity are the hallmarks of the fashion industry, especially in terms of price variation, size, and brand. To help the process of making more accurate business decisions, product segmentation is needed to identify the characteristics of each group. This study utilizes the K-Means Clustering algorithm to group fashion products based on these attributes. The implementation is carried out using the RapidMiner platform, starting with the data normalization stage and the transformation of categorical attributes into numeric form. The optimal number of clusters is determined through the elbow method approach, which shows a significant decrease in the average distance between data in the cluster. The clustering results show the formation of product groups with different characteristics, which can be utilized in stock planning and marketing strategies. This study confirms that the K-Means algorithm is effective in analyzing the distribution of fashion products based on the main attributes they have.]]>
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