An optimized product model based on the K-means++ algorithm

To precisely uncover the primary sales patterns of supermarket vegetable products, this study leverages detailed sales data from a supermarket spanning nearly three years. The K-means++ clustering algorithm was employed to conduct an in-depth analysis of the sales correlation among individual vegetable categories. The clustering results scientifically categorize vegetable items into three groups: fast-moving, average-moving, and slow-moving. This classification not only enhances the supermarket's precise understanding of the sales performance of each individual product but also provides robust data support and reference for subsequent decision-making in areas such as inventory replenishment strategies and product pricing. Ultimately, it aids in optimizing inventory management, improving operational efficiency, and ultimately enhancing the supermarket's overall profit margins.