A naïve implementation of the nearest neighbor algorithm is quite slow—it checks all pairs of points to find those that are close together. Better implementations exist, with some implemented in scikit-learn.
URL: https://github.com/jnothman/scikit-learn/tree/pr2532
For instance, a kd-tree can be created that speeds up the algorithm (and this is already included in scikit-learn).
Another way to speed up this search is to use locality-sensitive hashing, Locality-Sensitive Hashing (LSH). This is a proposed improvement for scikit-learn, and hasn't made it into the package at the time of writing. The preceding link gives a development branch of scikit-learn that will allow you to test out LSH on a dataset. Read through the documentation attached to this branch for details on doing this.
To install it, clone the repository and follow the instructions to install the Bleeding Edge code available at http://scikit-learn.org...
