ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions.
In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski metrics.
These include the well known Euclidean distance, Manhattan distance,
and max distance.
Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it anyway.)
The library implements a number of different data structures, based
on kd-trees and box-decomposition trees, and employs a couple of different
search strategies.
The library also comes with test programs for measuring the quality
of performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.
