I did a modified version of this once for a map of auto dealerships, although rather than working with a fixed k, I used a fix threshold for cluster distance. The algorithm I was working with had O(n³) complexity so to keep the pregeneration of clusters manageable, I partitioned data by state. The other fun part was finding the right metric formula for measuring distances. Because clusters needed to correspond to the rectangular view window on the map, rather than a standard Euclidean distance, I used d = max(Δx,Δy) which gives square neighborhoods rather than round ones.