nng: Nearest Neighbor Graphs

In cccd: Class Cover Catch Digraphs

Nearest Neighbor Graphs

Description

nearest neighbor, k-nearest neighbor, and mutual k-nearest neighbor (di)graphs.

Usage

nng(x = NULL, dx = NULL, k = 1, mutual = FALSE, method = NULL, 
    use.fnn = FALSE, algorithm = 'cover_tree')

Arguments

x	a data matrix. Either x or dx is required
dx	interpoint distance matrix
k	number of neighbors
mutual	logical. if true the neighbors must be mutual. See details.
method	the method used for the distance. See dist
use.fnn	logical. If TRUE, get.knn from the FNN package is used to obtain the neighbors.
algorithm	see get.knn.

Details

a k-nearest neighbor graph is a digraph where each vertex is associated with an observation and there is a directed edge between the vertex and it's k nearest neighbors. A mutual k-nearest neighbor graph is a graph where there is an edge between x and y if x is one of the k nearest neighbors of y AND y is one of the k nearest neighbors of x.

Value

an object of class igraph with the extra attributes

layout	the x vectors.
k,mutual,p	arguments given to nn.

Author(s)

David J. Marchette [e-mail]

References

D.J. Marchette, Random Graphs for Statistical Pattern Recognition, John Wiley & Sons, 2004.

See Also

dist get.knn

Examples

x <- matrix(runif(100),ncol=2)

G1 <- nng(x,k=1)
## Not run: 
par(mfrow=c(2,2))
plot(G1)

## End(Not run)

G2 <- nng(x,k=2)
## Not run: 
plot(G2)

## End(Not run)

G5 <- nng(x,k=5)
## Not run: 
plot(G5)

## End(Not run)

G5m <- nng(x,k=5,mutual=TRUE)
## Not run: 
plot(G5m)
par(mfrow=c(1,1))

## End(Not run)

cccd documentation built on April 8, 2022, 5:08 p.m.