I’m thinking of the following problem. I have a set of points in the plane which have either been pre-selected or were obtained by a clustering method (i.e., K-means). I thus have an associated Voronoi partition of the plane using either the k-means points or the pre-selected points. Now, I have a new point and I want to identify which of the Voronoi cells it is associated with. In either case, is there a ‘‘fast’’ method for doing this, or must it be done by a computing all the distances and finding the minimum (i.e., making it a linear in time cost)?
Related topics
| Topic | Replies | Views | Activity | |
|---|---|---|---|---|
| Voronoi cell index identification based on point(x,y) position | 1 | 66 | August 15, 2024 | |
| Extract neighbors from first shell of Voronoi tesselation | 3 | 632 | July 11, 2020 | |
| How to compute neighbour network using NearestNeighbors.jl | 4 | 1557 | January 23, 2017 | |
| Closest distance between two Euclidian grids | 19 | 1390 | June 5, 2021 | |
| Optimising voronoi computation | 4 | 170 | September 12, 2024 |