Facility location is one of the most critical factors in urban logistics planning, and the newspaper industry is no exception. Given the time-sensitive nature of newspapers and their narrow delivery time windows, efficient distribution network planning becomes essential. This research addresses the micro-hub location problem within the context of newspaper distribution across the Área Metropolitana del Valle de Aburrá in Medellin, Colombia, by developing a novel hybrid clustering strategy. We compare five clustering techniques: K-means, K-medians, K-medoids, Agglomerative Nesting (AGNES), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our strategy first uses AGNES (with single-linkage) to identify high-density regions and subsequently applies K-medoids within these identified areas to form compact clusters. Results demonstrated the superiority of the hybrid clustering strategy over both K-means and the individual clustering techniques. The hybrid approach generates more cohesive clusters, as evidenced by superior silhouette coefficients and within-cluster variance. The clustering proposal allowed 90% of customers to be located within 1.6 kilometers of a micro-hub, improving the distribution of newspapers in the urban areas.