# get.knn {FNN}

### Description

Fast k-nearest neighbor searching algorithms including a kd-tree, cover-tree and the algorithm implemented in class package.

### Usage

get.knn(data, k=10, algorithm=c("kd_tree", "cover_tree", "CR", "brute")) get.knnx(data, query, k=10, algorithm=c("kd_tree", "cover_tree", "CR", "brute"))

### Arguments

- data
- an input data matrix.
- query
- a query data matrix.
- algorithm
- nearest neighbor searching algorithm.
- k
- the maximum number of nearest neighbors to search. The default value is set to 10.

### Details

The *cover tree* is O(n) space data structure which allows us to answer queries in the same O(log(n)) time as *kd tree* given a fixed intrinsic dimensionality. Templated code from http://hunch.net/~jl/projects/cover_tree/cover_tree.html is used.

The *kd tree* algorithm is implemented in the Approximate Near Neighbor (ANN) C++ library (see http://www.cs.umd.edu/~mount/ANN/). The exact nearest neighbors are searched in this package. The *CR* algorithm is the *VR* using distance *1-x'y* assuming `x`

and `y`

are unit vectors. The *brute* algorithm searches linearly. It is a naive method.

### Values

a list contains:

- nn.index
- an n x k matrix for the nearest neighbor indice.
- nn.dist
- an n x k matrix for the nearest neighbor Euclidean distances.

### References

Bentley J.L. (1975), “Multidimensional binary search trees used for associative search,” *Communication ACM*, **18**, 309-517.

Arya S. and Mount D.M. (1993), “Approximate nearest neighbor searching,” *Proc. 4th Ann. ACM-SIAM Symposium on Discrete Algorithms (SODA'93)*, 271-280.

Arya S., Mount D.M., Netanyahu N.S., Silverman R. and Wu A.Y. (1998), “An optimal algorithm for approximate nearest neighbor searching,” *Journal of the ACM*, **45**, 891-923.

Beygelzimer A., Kakade S. and Langford J. (2006), “Cover trees for nearest neighbor,” *ACM Proc. 23rd international conference on Machine learning*, **148**, 97-104.

### See Also

`nn2`

in RANN, `ann`

in yaImpute and `knn`

in class.

### Examples

Documentation reproduced from package FNN, version 1.1. License: GPL (>= 2.1)