FNN (version 1.1.4.1)

get.knn: Search Nearest Neighbors

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"))

Value

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.

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.

Author

Shengqiao Li. To report any bugs or suggestions please email:

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 https://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.

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

  data<- query<- cbind(1:10, 1:10)

  get.knn(data, k=5)
  get.knnx(data, query, k=5)
  get.knnx(data, query, k=5, algo="kd_tree")

  th<- runif(10, min=0, max=2*pi)
  data2<-  cbind(cos(th), sin(th))
  get.knn(data2, k=5, algo="CR")