Visit Recommendation Model: Recursive K-Means Clustering Analysis of Retail Sales Data

Kristanto, Bagus Kristomoyo
Putri Listio, Syntia Widyayuningtias
Amien, Mukhlis
Baskoro, Panji Iman

JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Vol. 8 No. 1 (2024): July 2024

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.