kNN | 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 ) ## S3 method for class 'kNN' sort(x, decreasing = FALSE, ...) ## S3 method for class 'kNN' adjacencylist(x, ...) ## S3 method for class 'kNN' print(x, ...)
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 |
sort |
sort the neighbors by distance? Note that some search methods
already sort the results. Sorting is expensive and |
search |
nearest neighbor search strategy (one of |
bucketSize |
max size of the kd-tree leafs. |
splitRule |
rule to split the kd-tree. One of |
approx |
use approximate nearest neighbors. All NN up to a distance of
a factor of |
decreasing |
sort in decreasing order? |
... |
further arguments |
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
(subclass of NN) containing a
list with the following components:
dist |
a matrix with distances. |
id |
a matrix with |
k |
number |
Michael Hahsler
David M. Mount and Sunil Arya (2010). ANN: A Library for Approximate Nearest Neighbor Searching, http://www.cs.umd.edu/~mount/ANN/.
Other NN functions:
NN
,
comps()
,
frNN()
,
kNNdist()
,
sNN()
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.