Application of Hierarchical Clustering and K-means Clustering in Location Selection of Rural Logistics Center

Abstract

For rural reasonable logistics center site selection, combined with the characteristics and conditions of the rural road network in rural areas of logistics, based on Python hierarchical clustering algorithm and implementation of K-means clustering algorithm, established a logistics center selected rural road network reality Address model. Clustering points need to be positioned. Firstly, the number of suitable initial cluster centers is determined by hierarchical clustering; then, the number of selected initial cluster centers is used, and the K-means algorithm is used to cluster clusters based on Euclidean distance similarity. Iterative clustering is completed after three actual iterations. As a result, the cluster center is three logistics centers, and the problem of visualizing the location of the rural logistics center is realized. Finally, an example is given to verify that the method of solving the location problem of rural logistics center is feasible.