kdtree2-readme

kd tree java implementation 2

KDTREE 2: Fortran 95 and C++ software to efficiently search for near
neighbors in a multi-dimensional Euclidean space.

\author{Matthew B. Kennel}

\affiliation{Institute for Nonlinear Science,
             University of California, San Diego}

\begin{abstract}
Many data-based statistical algorithms require that one find \textit{near
or nearest neighbors} to a given vector among a set of points in that
vector space, usually with Euclidean topology. The k-d data structure
and search algorithms are the generalization of classical binary search
trees to higher dimensional spaces, so that one may locate near neighbors
to an example vector in $O(\log N)$ time instead of the brute-force
$O(N)$ time, with $N$ being the size of the data base. KDTREE2 is
a Fortran 95 module, and a parallel set of C++ classes which implement
tree construction and search routines to find either a set of $m$
nearest neighbors to an example, or all the neighbors within some
Euclidean distance $r.$ The two versions are independent and function
fully on their own. Considerable care has been taken in the implementation
of the search methods, resulting in substantially higher computational
efficiency (up to an order of magnitude faster) than the author's
previous Internet-distributed version. Architectural improvements
include rearrangement for memory cache-friendly performance, heap-based
priority queues for large $m$searches, and more effective pruning
of search paths by geometrical constraints to avoid wasted effort.
The improvements are the most potent in the more difficult and slowest
cases: larger data base sizes, higher dimensionality manifolds containing
the data set, and larger numbers of neighbors to search for. The C++
implementation requires the Standard Template Library as well as the
BOOST C++ library be installed. 
\end{abstract}