knn.index: Search Nearest Neighbors

In FNN: Fast Nearest Neighbor Search Algorithms and Applications

Search Nearest Neighbors

Description

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

Usage

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

Value

return the indice of k nearest neighbors.

Author(s)

Shengqiao Li. To report any bugs or suggestions please email: [e-mail]

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

knn.dist and get.knn.

Examples

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

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

FNN documentation built on Sept. 22, 2024, 9:06 a.m.