This research applies and modifies K-means clustering analysis from Data Mining to solving the location problem. First, a case study of Thailand’s convenience store franchise in locating distribution centers (DCs) is conducted. Then, the final centroids are served at suggested DC locations. Besides the typical distance, Euclidean, used in K-means, Manhattan, and Chebyshev, is also experimented with. Moreover, due to the stores’ different demands, a modification of the centroid calculation is needed to reflect the center-of-gravity effects. For the proposed centroid calculation, the above three distance metrics incorporating the demands as weights give rise to another three approaches and are thus named Weighted Euclidean, Weighted Manhattan, and Weighted Chebyshev, respectively. Besides the optimal locations, the effectiveness of these six clustering approaches is measured by the expected total distribution cost from DCs to their served stores and the expected Davies–Bouldin index (DBI). Concurrently, the efficiency is measured by the expected number of iterations to the final clusters. All these six clustering approaches are then implemented in the case study of locating eight DCs to distribute to 260 convenience stores in Eastern Thailand. The results show that though all approaches yield locations in close proximity, the Weighted Chebyshev is the most effective one having both the lowest expected distribution cost and lowest expected DBI. In contrast, Euclidean is the most efficient approach, with the lowest expected number of iterations to the final clusters, followed by Weighted Chebyshev. Therefore, the DC locations from Weighted Chebyshev could, ultimately, be chosen for this Thailand’s convenience store franchise.

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