3 ms·

Yes, but that's harder to implement. Spherical kmeans is much simpler. A simple cos distance between vectors.

• dhosek 6y ago
  I don't remember exactly what I did or how it maps to spherical k-means. One modification was to use a version of the k-means algorithm but doing clustering not by fixed size but by fixed distance. The other was measuring distance by what the map coordinates would be rather than the lat/long coordinates. The last thing was rather than using a euclidean distance which gives circular neighborhoods, we used max(∆x, ∆y) which gives square neighborhoods. Clusters were pre-calculated or else we would have made rectangular neighborhoods with the proportions of the viewport window on the map. In retrospect, it might have made sense to do client-side clustering for more detailed zoom levels where n would have been relatively small and we could have gotten that as well.