| kNN {dbscan} | R Documentation |
This function uses a kd-tree to find all k nearest neighbors in a data matrix (including distances) fast.
kNN(x, k, query = NULL, sort = TRUE, search = "kdtree", bucketSize = 10, splitRule = "suggest", approx = 0)
x |
a data matrix, a dist object or a kNN object. |
k |
number of neighbors to find. |
query |
a data matrix with the points to query. If query is not specified, the NN for all the points in |
search |
nearest neighbor search strategy (one of "kdtree", "linear" or "dist"). |
sort |
sort the neighbors by distance? Note that some search methods already sort the results. Sorting is expensive and
|
bucketSize |
max size of the kd-tree leafs. |
splitRule |
rule to split the kd-tree. One of "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR" or "SUGGEST" (SL stands for sliding). "SUGGEST" uses ANNs best guess. |
approx |
use approximate nearest neighbors. All NN up to a distance of
a factor of 1+ |
Ties: If the kth and the (k+1)th nearest neighbor are tied, then the neighbor found first is returned and the other one is ignored.
Self-matches: If no query is specified, then self-matches are removed.
Details on the search parameters:
search controls if a kd-tree or linear search (both implemented in
the ANN library; see Mount and Arya, 2010). Note, that these implementations cannot handle NAs. search="dist" precomputes Euclidean distances first using R.
NAs are handled, but the resulting distance matrix cannot contain NAs. To use other distance measures, a precomputed distance matrix can be
provided as x (search is ignored).
bucketSize and splitRule influence how the kd-tree is built.
approx uses the approximate nearest neighbor search implemented in ANN.
All nearest neighbors up to a distance of eps/(1+approx)
will be considered and all with a distance greater than eps will not
be considered. The other points might be considered. Note that this results in
some actual nearest neighbors being omitted leading to spurious clusters and noise points. However, the algorithm will enjoy a significant speedup. For more details see Mount and Arya (2010).
An object of class kNN containing a list with the following components:
dist |
a matrix with distances. |
id |
a matrix with ids. |
k |
number of k used. |
Michael Hahsler
David M. Mount and Sunil Arya (2010). ANN: A Library for Approximate Nearest Neighbor Searching, http://www.cs.umd.edu/~mount/ANN/.
NN and frNN for fixed radius nearest neighbors.
data(iris) x <- iris[, -5] # Example 1: finding kNN for all points in a data matrix (using a kd-tree) nn <- kNN(x, k = 5) nn # explore neighborhood of point 10 i <- 10 nn$id[i,] plot(x, col = ifelse(1:nrow(iris) %in% nn$id[i,], "red", "black")) # visualize the 5 nearest neighbors plot(nn, x) # visualize a reduced 2-NN graph plot(kNN(nn, k = 2), x) # Example 2: find kNN for query points q <- x[c(1,100),] nn <- kNN(x, k = 10, query = q) plot(nn, x, col = "grey") points(q, pch = 3, lwd = 2) # Example 3: find kNN using distances d <- dist(x, method = "manhattan") nn <- kNN(d, k = 1) plot(nn, x)