We have boring CSV with 10000 rows of ages (float), titles (enum/int), scores (float), .... We have N columns each with int/float values in a table. You can imagine this as points in ND space We want to pick K points that would have maximised distance between each other. So if we have 100 points in a tightly packed cluster and one point in the distance we would get something like this for three points: or this For 4 points it will become more interesting and pick some point in the middle. So how to select K most distant rows (points) from N (with any complexity)? It looks like an ND point cloud "triangulation" with a given resolution yet not for 3d points. I search for a reasonably fast approach (approximate - no precise solution needed) for K=200 and N=100000 and ND=6 (probably multigrid or ANN on KDTree based, SOM or triangulation based..).. Does anyone know one? Continue reading...
