Visit Recommendation Model: Cluster Analysis of Retail Sales Data using Recursive K-Means Clustering Method
Abstract
In the context of retail distribution, this study employs recursive K-means clustering on retail sales data to optimize clusters of nearest-distance stores for salesperson route recommendations. This approach addresses the stochastic salesperson problem by generating effective routes, enhancing cost reduction, and improving service efficiency. The recursive K-means algorithm dynamically adjusts to continuous changes in store numbers, locations, and transaction data. Consequently, this research successfully developed a model that automatically re-clusters the data with each change, providing continuously updated and effective store recommendations.
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References
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